Chapter 4
2023-08-29
Load Packages
library(ggplot2)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(dplyr)
library(meta)## Loading 'meta' package (version 6.5-0).
## Type 'help(meta)' for a brief overview.
## Readers of 'Meta-Analysis with R (Use R!)' should install
## older version of 'meta' package: https://tinyurl.com/dt4y5drslibrary(PRISMAstatement)
library(skimr)
library(MASS)##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## selectlibrary(ggpubr)setwd("~/Desktop/Chapter 4")
photo <- read.csv("observations_2022.csv")
summary(photo)## region site site_code microsite
## Length:58015 Length:58015 Length:58015 Length:58015
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## plot cam_ID month day
## Min. :1.000 Min. :1.000 Length:58015 Min. : 3.00
## 1st Qu.:1.000 1st Qu.:1.000 Class :character 1st Qu.: 6.00
## Median :1.000 Median :2.000 Mode :character Median :16.00
## Mean :1.775 Mean :1.527 Mean :13.98
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:22.00
## Max. :4.000 Max. :2.000 Max. :29.00
## year shrub_density rep identified_by
## Min. :2022 Min. : 0.000 Min. : 1 Length:58015
## 1st Qu.:2022 1st Qu.: 0.000 1st Qu.: 5386 Class :character
## Median :2022 Median : 0.000 Median :12638 Mode :character
## Mean :2022 Mean : 4.681 Mean :15170
## 3rd Qu.:2022 3rd Qu.:11.000 3rd Qu.:24318
## Max. :2022 Max. :14.000 Max. :38822
## filename timestamp animal.hit class
## Length:58015 Length:58015 Min. :0.00000 Length:58015
## Class :character Class :character 1st Qu.:0.00000 Class :character
## Mode :character Mode :character Median :0.00000 Mode :character
## Mean :0.06521
## 3rd Qu.:0.00000
## Max. :1.00000
## order family genus species
## Length:58015 Length:58015 Length:58015 Length:58015
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## common_name number_of_objects
## Length:58015 Min. : 1.000
## Class :character 1st Qu.: 1.000
## Mode :character Median : 1.000
## Mean : 1.001
## 3rd Qu.: 1.000
## Max. :12.000photo <- photo %>%
filter(common_name != "Human")
photo <- photo %>%
filter(common_name != "Human-Camera Trapper")
photo <- photo %>%
filter(common_name != "Domestic Dog")
photo <- photo %>%
filter(common_name != "Vehicle")
photo <- photo %>%
dplyr::filter(common_name != "Insect")
photo <- photo %>%
dplyr::filter(common_name != "Animal")
photo <- photo %>%
dplyr::filter(common_name != "Bird")
photo <- photo %>%
filter(common_name != "No CV Result")
count.hit <- photo %>%
count(animal.hit) %>%
na.omit()
summary(count.hit)## animal.hit n
## Min. :0.00 Min. : 3169
## 1st Qu.:0.25 1st Qu.:15935
## Median :0.50 Median :28700
## Mean :0.50 Mean :28700
## 3rd Qu.:0.75 3rd Qu.:41466
## Max. :1.00 Max. :54232### 2022 Had a 5.88% catch rate### Animal Observations by Site_Code
animals_by_sitecode <- photo%>%
group_by(site_code, microsite, common_name) %>%
summarise(captures = sum(animal.hit), n = n())## `summarise()` has grouped output by 'site_code', 'microsite'. You can override
## using the `.groups` argument.animals_by_sitecode <- animals_by_sitecode %>%
filter(common_name != "Blank") ### Animal observations by Site
animals_by_site <- photo %>% group_by(site,microsite,common_name) %>% summarise(captures = sum(animal.hit))## `summarise()` has grouped output by 'site', 'microsite'. You can override using
## the `.groups` argument.animals_by_site <- animals_by_site %>% filter(common_name != "Blank")### Animal observations by Density
animals_by_density <- photo %>% group_by(microsite,common_name) %>% summarise(captures = sum(animal.hit))## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.animals_by_density <- animals_by_density %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")### Total Observations 2022
Total_Observations <- photo %>% group_by(common_name) %>% summarise(total = sum(animal.hit)) %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")density_obvs <- merge(animals_by_density, Total_Observations, all = TRUE)
density_obvs$percent_presence <- density_obvs$captures/density_obvs$total### Percent proportion Figure
plot1 <- ggplot(density_obvs, aes(common_name, percent_presence, fill = microsite)) + geom_bar(stat = "identity") + coord_flip() + theme_classic() + scale_x_discrete(limits=rev) + xlab("Species") + ylab("Percent Proportion") + labs(fill = "Microsite")
plot1 + scale_fill_manual(values = c("#009900", "#0066cc"))
library(emmeans)
m1 <- glm(total ~ microsite*common_name, family = "poisson", data = density_obvs)
anova(m1, test = "Chisq")## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: total
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 44 22806
## microsite 1 5.4 43 22801 0.01984 *
## common_name 26 22800.9 17 0 < 2e-16 ***
## microsite:common_name 17 0.0 0 0 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e1 <- emmeans(m1, pairwise~common_name)## NOTE: Results may be misleading due to involvement in interactionse1## $emmeans
## common_name emmean SE df asymp.LCL asymp.UCL
## American Robin nonEst NA NA NA NA
## Black-tailed Jackrabbit 5.442 0.04652 Inf 5.3512 5.534
## Blunt-nosed Leopard Lizard 1.099 0.40825 Inf 0.2985 1.899
## Bobcat 1.792 0.28868 Inf 1.2260 2.358
## Brewer's Blackbird 2.485 0.20412 Inf 2.0848 2.885
## California Ground Squirrel 5.442 0.04652 Inf 5.3512 5.534
## California Pocket Mouse 1.386 0.35355 Inf 0.6933 2.079
## California Quail 3.178 0.14434 Inf 2.8952 3.461
## California Thrasher nonEst NA NA NA NA
## Common Raven 3.989 0.09623 Inf 3.8004 4.178
## Coyote 4.489 0.07495 Inf 4.3417 4.636
## Desert Cottontail 4.078 0.09206 Inf 3.8971 4.258
## Desert Iguana nonEst NA NA NA NA
## Giant Kangaroo Rat 4.078 0.09206 Inf 3.8971 4.258
## Great White Egret nonEst NA NA NA NA
## Greater Roadrunner 1.946 0.26726 Inf 1.4221 2.470
## Heermann's Kangaroo Rat 7.680 0.01520 Inf 7.6504 7.710
## Killdeer nonEst NA NA NA NA
## Kit Fox 2.079 0.25000 Inf 1.5895 2.569
## Lark Sparrow 1.946 0.26726 Inf 1.4221 2.470
## Loggerhead Shrike 2.079 0.25000 Inf 1.5895 2.569
## Mohave Ground Squirrel nonEst NA NA NA NA
## Mourning Dove 1.609 0.31623 Inf 0.9896 2.229
## Nelson's Antelope Squirrel 4.956 0.05934 Inf 4.8395 5.072
## Red-tailed Hawk nonEst NA NA NA NA
## Salinas Pocket Mouse nonEst NA NA NA NA
## Vesper Sparrow nonEst NA NA NA NA
##
## Results are averaged over the levels of: microsite
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df
## American Robin - (Black-tailed Jackrabbit) nonEst NA NA
## American Robin - (Blunt-nosed Leopard Lizard) nonEst NA NA
## American Robin - Bobcat nonEst NA NA
## American Robin - Brewer's Blackbird nonEst NA NA
## American Robin - California Ground Squirrel nonEst NA NA
## American Robin - California Pocket Mouse nonEst NA NA
## American Robin - California Quail nonEst NA NA
## American Robin - California Thrasher nonEst NA NA
## American Robin - Common Raven nonEst NA NA
## American Robin - Coyote nonEst NA NA
## American Robin - Desert Cottontail nonEst NA NA
## American Robin - Desert Iguana nonEst NA NA
## American Robin - Giant Kangaroo Rat nonEst NA NA
## American Robin - Great White Egret nonEst NA NA
## American Robin - Greater Roadrunner nonEst NA NA
## American Robin - Heermann's Kangaroo Rat nonEst NA NA
## American Robin - Killdeer nonEst NA NA
## American Robin - Kit Fox nonEst NA NA
## American Robin - Lark Sparrow nonEst NA NA
## American Robin - Loggerhead Shrike nonEst NA NA
## American Robin - Mohave Ground Squirrel nonEst NA NA
## American Robin - Mourning Dove nonEst NA NA
## American Robin - Nelson's Antelope Squirrel nonEst NA NA
## American Robin - (Red-tailed Hawk) nonEst NA NA
## American Robin - Salinas Pocket Mouse nonEst NA NA
## American Robin - Vesper Sparrow nonEst NA NA
## (Black-tailed Jackrabbit) - (Blunt-nosed Leopard Lizard) 4.3438 0.4109 Inf
## (Black-tailed Jackrabbit) - Bobcat 3.6507 0.2924 Inf
## (Black-tailed Jackrabbit) - Brewer's Blackbird 2.9575 0.2094 Inf
## (Black-tailed Jackrabbit) - California Ground Squirrel 0.0000 0.0658 Inf
## (Black-tailed Jackrabbit) - California Pocket Mouse 4.0561 0.3566 Inf
## (Black-tailed Jackrabbit) - California Quail 2.2644 0.1517 Inf
## (Black-tailed Jackrabbit) - California Thrasher nonEst NA NA
## (Black-tailed Jackrabbit) - Common Raven 1.4534 0.1069 Inf
## (Black-tailed Jackrabbit) - Coyote 0.9538 0.0882 Inf
## (Black-tailed Jackrabbit) - Desert Cottontail 1.3649 0.1031 Inf
## (Black-tailed Jackrabbit) - Desert Iguana nonEst NA NA
## (Black-tailed Jackrabbit) - Giant Kangaroo Rat 1.3649 0.1031 Inf
## (Black-tailed Jackrabbit) - Great White Egret nonEst NA NA
## (Black-tailed Jackrabbit) - Greater Roadrunner 3.4965 0.2713 Inf
## (Black-tailed Jackrabbit) - Heermann's Kangaroo Rat -2.2378 0.0489 Inf
## (Black-tailed Jackrabbit) - Killdeer nonEst NA NA
## (Black-tailed Jackrabbit) - Kit Fox 3.3630 0.2543 Inf
## (Black-tailed Jackrabbit) - Lark Sparrow 3.4965 0.2713 Inf
## (Black-tailed Jackrabbit) - Loggerhead Shrike 3.3630 0.2543 Inf
## (Black-tailed Jackrabbit) - Mohave Ground Squirrel nonEst NA NA
## (Black-tailed Jackrabbit) - Mourning Dove 3.8330 0.3196 Inf
## (Black-tailed Jackrabbit) - Nelson's Antelope Squirrel 0.4866 0.0754 Inf
## (Black-tailed Jackrabbit) - (Red-tailed Hawk) nonEst NA NA
## (Black-tailed Jackrabbit) - Salinas Pocket Mouse nonEst NA NA
## (Black-tailed Jackrabbit) - Vesper Sparrow nonEst NA NA
## (Blunt-nosed Leopard Lizard) - Bobcat -0.6931 0.5000 Inf
## (Blunt-nosed Leopard Lizard) - Brewer's Blackbird -1.3863 0.4564 Inf
## (Blunt-nosed Leopard Lizard) - California Ground Squirrel -4.3438 0.4109 Inf
## (Blunt-nosed Leopard Lizard) - California Pocket Mouse -0.2877 0.5401 Inf
## (Blunt-nosed Leopard Lizard) - California Quail -2.0794 0.4330 Inf
## (Blunt-nosed Leopard Lizard) - California Thrasher nonEst NA NA
## (Blunt-nosed Leopard Lizard) - Common Raven -2.8904 0.4194 Inf
## (Blunt-nosed Leopard Lizard) - Coyote -3.3900 0.4151 Inf
## (Blunt-nosed Leopard Lizard) - Desert Cottontail -2.9789 0.4185 Inf
## (Blunt-nosed Leopard Lizard) - Desert Iguana nonEst NA NA
## (Blunt-nosed Leopard Lizard) - Giant Kangaroo Rat -2.9789 0.4185 Inf
## (Blunt-nosed Leopard Lizard) - Great White Egret nonEst NA NA
## (Blunt-nosed Leopard Lizard) - Greater Roadrunner -0.8473 0.4879 Inf
## (Blunt-nosed Leopard Lizard) - Heermann's Kangaroo Rat -6.5816 0.4085 Inf
## (Blunt-nosed Leopard Lizard) - Killdeer nonEst NA NA
## (Blunt-nosed Leopard Lizard) - Kit Fox -0.9808 0.4787 Inf
## (Blunt-nosed Leopard Lizard) - Lark Sparrow -0.8473 0.4879 Inf
## (Blunt-nosed Leopard Lizard) - Loggerhead Shrike -0.9808 0.4787 Inf
## (Blunt-nosed Leopard Lizard) - Mohave Ground Squirrel nonEst NA NA
## (Blunt-nosed Leopard Lizard) - Mourning Dove -0.5108 0.5164 Inf
## (Blunt-nosed Leopard Lizard) - Nelson's Antelope Squirrel -3.8572 0.4125 Inf
## (Blunt-nosed Leopard Lizard) - (Red-tailed Hawk) nonEst NA NA
## (Blunt-nosed Leopard Lizard) - Salinas Pocket Mouse nonEst NA NA
## (Blunt-nosed Leopard Lizard) - Vesper Sparrow nonEst NA NA
## Bobcat - Brewer's Blackbird -0.6931 0.3536 Inf
## Bobcat - California Ground Squirrel -3.6507 0.2924 Inf
## Bobcat - California Pocket Mouse 0.4055 0.4564 Inf
## Bobcat - California Quail -1.3863 0.3227 Inf
## Bobcat - California Thrasher nonEst NA NA
## Bobcat - Common Raven -2.1972 0.3043 Inf
## Bobcat - Coyote -2.6969 0.2982 Inf
## Bobcat - Desert Cottontail -2.2858 0.3030 Inf
## Bobcat - Desert Iguana nonEst NA NA
## Bobcat - Giant Kangaroo Rat -2.2858 0.3030 Inf
## Bobcat - Great White Egret nonEst NA NA
## Bobcat - Greater Roadrunner -0.1542 0.3934 Inf
## Bobcat - Heermann's Kangaroo Rat -5.8884 0.2891 Inf
## Bobcat - Killdeer nonEst NA NA
## Bobcat - Kit Fox -0.2877 0.3819 Inf
## Bobcat - Lark Sparrow -0.1542 0.3934 Inf
## Bobcat - Loggerhead Shrike -0.2877 0.3819 Inf
## Bobcat - Mohave Ground Squirrel nonEst NA NA
## Bobcat - Mourning Dove 0.1823 0.4282 Inf
## Bobcat - Nelson's Antelope Squirrel -3.1641 0.2947 Inf
## Bobcat - (Red-tailed Hawk) nonEst NA NA
## Bobcat - Salinas Pocket Mouse nonEst NA NA
## Bobcat - Vesper Sparrow nonEst NA NA
## Brewer's Blackbird - California Ground Squirrel -2.9575 0.2094 Inf
## Brewer's Blackbird - California Pocket Mouse 1.0986 0.4082 Inf
## Brewer's Blackbird - California Quail -0.6931 0.2500 Inf
## Brewer's Blackbird - California Thrasher nonEst NA NA
## Brewer's Blackbird - Common Raven -1.5041 0.2257 Inf
## Brewer's Blackbird - Coyote -2.0037 0.2175 Inf
## Brewer's Blackbird - Desert Cottontail -1.5926 0.2239 Inf
## Brewer's Blackbird - Desert Iguana nonEst NA NA
## Brewer's Blackbird - Giant Kangaroo Rat -1.5926 0.2239 Inf
## Brewer's Blackbird - Great White Egret nonEst NA NA
## Brewer's Blackbird - Greater Roadrunner 0.5390 0.3363 Inf
## Brewer's Blackbird - Heermann's Kangaroo Rat -5.1953 0.2047 Inf
## Brewer's Blackbird - Killdeer nonEst NA NA
## Brewer's Blackbird - Kit Fox 0.4055 0.3227 Inf
## Brewer's Blackbird - Lark Sparrow 0.5390 0.3363 Inf
## Brewer's Blackbird - Loggerhead Shrike 0.4055 0.3227 Inf
## Brewer's Blackbird - Mohave Ground Squirrel nonEst NA NA
## Brewer's Blackbird - Mourning Dove 0.8755 0.3764 Inf
## Brewer's Blackbird - Nelson's Antelope Squirrel -2.4709 0.2126 Inf
## Brewer's Blackbird - (Red-tailed Hawk) nonEst NA NA
## Brewer's Blackbird - Salinas Pocket Mouse nonEst NA NA
## Brewer's Blackbird - Vesper Sparrow nonEst NA NA
## California Ground Squirrel - California Pocket Mouse 4.0561 0.3566 Inf
## California Ground Squirrel - California Quail 2.2644 0.1517 Inf
## California Ground Squirrel - California Thrasher nonEst NA NA
## California Ground Squirrel - Common Raven 1.4534 0.1069 Inf
## California Ground Squirrel - Coyote 0.9538 0.0882 Inf
## California Ground Squirrel - Desert Cottontail 1.3649 0.1031 Inf
## California Ground Squirrel - Desert Iguana nonEst NA NA
## California Ground Squirrel - Giant Kangaroo Rat 1.3649 0.1031 Inf
## California Ground Squirrel - Great White Egret nonEst NA NA
## California Ground Squirrel - Greater Roadrunner 3.4965 0.2713 Inf
## California Ground Squirrel - Heermann's Kangaroo Rat -2.2378 0.0489 Inf
## California Ground Squirrel - Killdeer nonEst NA NA
## California Ground Squirrel - Kit Fox 3.3630 0.2543 Inf
## California Ground Squirrel - Lark Sparrow 3.4965 0.2713 Inf
## California Ground Squirrel - Loggerhead Shrike 3.3630 0.2543 Inf
## California Ground Squirrel - Mohave Ground Squirrel nonEst NA NA
## California Ground Squirrel - Mourning Dove 3.8330 0.3196 Inf
## California Ground Squirrel - Nelson's Antelope Squirrel 0.4866 0.0754 Inf
## California Ground Squirrel - (Red-tailed Hawk) nonEst NA NA
## California Ground Squirrel - Salinas Pocket Mouse nonEst NA NA
## California Ground Squirrel - Vesper Sparrow nonEst NA NA
## California Pocket Mouse - California Quail -1.7918 0.3819 Inf
## California Pocket Mouse - California Thrasher nonEst NA NA
## California Pocket Mouse - Common Raven -2.6027 0.3664 Inf
## California Pocket Mouse - Coyote -3.1023 0.3614 Inf
## California Pocket Mouse - Desert Cottontail -2.6912 0.3653 Inf
## California Pocket Mouse - Desert Iguana nonEst NA NA
## California Pocket Mouse - Giant Kangaroo Rat -2.6912 0.3653 Inf
## California Pocket Mouse - Great White Egret nonEst NA NA
## California Pocket Mouse - Greater Roadrunner -0.5596 0.4432 Inf
## California Pocket Mouse - Heermann's Kangaroo Rat -6.2939 0.3539 Inf
## California Pocket Mouse - Killdeer nonEst NA NA
## California Pocket Mouse - Kit Fox -0.6931 0.4330 Inf
## California Pocket Mouse - Lark Sparrow -0.5596 0.4432 Inf
## California Pocket Mouse - Loggerhead Shrike -0.6931 0.4330 Inf
## California Pocket Mouse - Mohave Ground Squirrel nonEst NA NA
## California Pocket Mouse - Mourning Dove -0.2231 0.4743 Inf
## California Pocket Mouse - Nelson's Antelope Squirrel -3.5695 0.3585 Inf
## California Pocket Mouse - (Red-tailed Hawk) nonEst NA NA
## California Pocket Mouse - Salinas Pocket Mouse nonEst NA NA
## California Pocket Mouse - Vesper Sparrow nonEst NA NA
## California Quail - California Thrasher nonEst NA NA
## California Quail - Common Raven -0.8109 0.1735 Inf
## California Quail - Coyote -1.3106 0.1626 Inf
## California Quail - Desert Cottontail -0.8995 0.1712 Inf
## California Quail - Desert Iguana nonEst NA NA
## California Quail - Giant Kangaroo Rat -0.8995 0.1712 Inf
## California Quail - Great White Egret nonEst NA NA
## California Quail - Greater Roadrunner 1.2321 0.3037 Inf
## California Quail - Heermann's Kangaroo Rat -4.5021 0.1451 Inf
## California Quail - Killdeer nonEst NA NA
## California Quail - Kit Fox 1.0986 0.2887 Inf
## California Quail - Lark Sparrow 1.2321 0.3037 Inf
## California Quail - Loggerhead Shrike 1.0986 0.2887 Inf
## California Quail - Mohave Ground Squirrel nonEst NA NA
## California Quail - Mourning Dove 1.5686 0.3476 Inf
## California Quail - Nelson's Antelope Squirrel -1.7778 0.1561 Inf
## California Quail - (Red-tailed Hawk) nonEst NA NA
## California Quail - Salinas Pocket Mouse nonEst NA NA
## California Quail - Vesper Sparrow nonEst NA NA
## California Thrasher - Common Raven nonEst NA NA
## California Thrasher - Coyote nonEst NA NA
## California Thrasher - Desert Cottontail nonEst NA NA
## California Thrasher - Desert Iguana nonEst NA NA
## California Thrasher - Giant Kangaroo Rat nonEst NA NA
## California Thrasher - Great White Egret nonEst NA NA
## California Thrasher - Greater Roadrunner nonEst NA NA
## California Thrasher - Heermann's Kangaroo Rat nonEst NA NA
## California Thrasher - Killdeer nonEst NA NA
## California Thrasher - Kit Fox nonEst NA NA
## California Thrasher - Lark Sparrow nonEst NA NA
## California Thrasher - Loggerhead Shrike nonEst NA NA
## California Thrasher - Mohave Ground Squirrel nonEst NA NA
## California Thrasher - Mourning Dove nonEst NA NA
## California Thrasher - Nelson's Antelope Squirrel nonEst NA NA
## California Thrasher - (Red-tailed Hawk) nonEst NA NA
## California Thrasher - Salinas Pocket Mouse nonEst NA NA
## California Thrasher - Vesper Sparrow nonEst NA NA
## Common Raven - Coyote -0.4997 0.1220 Inf
## Common Raven - Desert Cottontail -0.0886 0.1332 Inf
## Common Raven - Desert Iguana nonEst NA NA
## Common Raven - Giant Kangaroo Rat -0.0886 0.1332 Inf
## Common Raven - Great White Egret nonEst NA NA
## Common Raven - Greater Roadrunner 2.0431 0.2841 Inf
## Common Raven - Heermann's Kangaroo Rat -3.6912 0.0974 Inf
## Common Raven - Killdeer nonEst NA NA
## Common Raven - Kit Fox 1.9095 0.2679 Inf
## Common Raven - Lark Sparrow 2.0431 0.2841 Inf
## Common Raven - Loggerhead Shrike 1.9095 0.2679 Inf
## Common Raven - Mohave Ground Squirrel nonEst NA NA
## Common Raven - Mourning Dove 2.3795 0.3305 Inf
## Common Raven - Nelson's Antelope Squirrel -0.9668 0.1131 Inf
## Common Raven - (Red-tailed Hawk) nonEst NA NA
## Common Raven - Salinas Pocket Mouse nonEst NA NA
## Common Raven - Vesper Sparrow nonEst NA NA
## Coyote - Desert Cottontail 0.4111 0.1187 Inf
## Coyote - Desert Iguana nonEst NA NA
## Coyote - Giant Kangaroo Rat 0.4111 0.1187 Inf
## Coyote - Great White Egret nonEst NA NA
## Coyote - Greater Roadrunner 2.5427 0.2776 Inf
## Coyote - Heermann's Kangaroo Rat -3.1915 0.0765 Inf
## Coyote - Killdeer nonEst NA NA
## Coyote - Kit Fox 2.4092 0.2610 Inf
## Coyote - Lark Sparrow 2.5427 0.2776 Inf
## Coyote - Loggerhead Shrike 2.4092 0.2610 Inf
## Coyote - Mohave Ground Squirrel nonEst NA NA
## Coyote - Mourning Dove 2.8792 0.3250 Inf
## Coyote - Nelson's Antelope Squirrel -0.4672 0.0956 Inf
## Coyote - (Red-tailed Hawk) nonEst NA NA
## Coyote - Salinas Pocket Mouse nonEst NA NA
## Coyote - Vesper Sparrow nonEst NA NA
## Desert Cottontail - Desert Iguana nonEst NA NA
## Desert Cottontail - Giant Kangaroo Rat 0.0000 0.1302 Inf
## Desert Cottontail - Great White Egret nonEst NA NA
## Desert Cottontail - Greater Roadrunner 2.1316 0.2827 Inf
## Desert Cottontail - Heermann's Kangaroo Rat -3.6026 0.0933 Inf
## Desert Cottontail - Killdeer nonEst NA NA
## Desert Cottontail - Kit Fox 1.9981 0.2664 Inf
## Desert Cottontail - Lark Sparrow 2.1316 0.2827 Inf
## Desert Cottontail - Loggerhead Shrike 1.9981 0.2664 Inf
## Desert Cottontail - Mohave Ground Squirrel nonEst NA NA
## Desert Cottontail - Mourning Dove 2.4681 0.3294 Inf
## Desert Cottontail - Nelson's Antelope Squirrel -0.8783 0.1095 Inf
## Desert Cottontail - (Red-tailed Hawk) nonEst NA NA
## Desert Cottontail - Salinas Pocket Mouse nonEst NA NA
## Desert Cottontail - Vesper Sparrow nonEst NA NA
## Desert Iguana - Giant Kangaroo Rat nonEst NA NA
## Desert Iguana - Great White Egret nonEst NA NA
## Desert Iguana - Greater Roadrunner nonEst NA NA
## Desert Iguana - Heermann's Kangaroo Rat nonEst NA NA
## Desert Iguana - Killdeer nonEst NA NA
## Desert Iguana - Kit Fox nonEst NA NA
## Desert Iguana - Lark Sparrow nonEst NA NA
## Desert Iguana - Loggerhead Shrike nonEst NA NA
## Desert Iguana - Mohave Ground Squirrel nonEst NA NA
## Desert Iguana - Mourning Dove nonEst NA NA
## Desert Iguana - Nelson's Antelope Squirrel nonEst NA NA
## Desert Iguana - (Red-tailed Hawk) nonEst NA NA
## Desert Iguana - Salinas Pocket Mouse nonEst NA NA
## Desert Iguana - Vesper Sparrow nonEst NA NA
## Giant Kangaroo Rat - Great White Egret nonEst NA NA
## Giant Kangaroo Rat - Greater Roadrunner 2.1316 0.2827 Inf
## Giant Kangaroo Rat - Heermann's Kangaroo Rat -3.6026 0.0933 Inf
## Giant Kangaroo Rat - Killdeer nonEst NA NA
## Giant Kangaroo Rat - Kit Fox 1.9981 0.2664 Inf
## Giant Kangaroo Rat - Lark Sparrow 2.1316 0.2827 Inf
## Giant Kangaroo Rat - Loggerhead Shrike 1.9981 0.2664 Inf
## Giant Kangaroo Rat - Mohave Ground Squirrel nonEst NA NA
## Giant Kangaroo Rat - Mourning Dove 2.4681 0.3294 Inf
## Giant Kangaroo Rat - Nelson's Antelope Squirrel -0.8783 0.1095 Inf
## Giant Kangaroo Rat - (Red-tailed Hawk) nonEst NA NA
## Giant Kangaroo Rat - Salinas Pocket Mouse nonEst NA NA
## Giant Kangaroo Rat - Vesper Sparrow nonEst NA NA
## Great White Egret - Greater Roadrunner nonEst NA NA
## Great White Egret - Heermann's Kangaroo Rat nonEst NA NA
## Great White Egret - Killdeer nonEst NA NA
## Great White Egret - Kit Fox nonEst NA NA
## Great White Egret - Lark Sparrow nonEst NA NA
## Great White Egret - Loggerhead Shrike nonEst NA NA
## Great White Egret - Mohave Ground Squirrel nonEst NA NA
## Great White Egret - Mourning Dove nonEst NA NA
## Great White Egret - Nelson's Antelope Squirrel nonEst NA NA
## Great White Egret - (Red-tailed Hawk) nonEst NA NA
## Great White Egret - Salinas Pocket Mouse nonEst NA NA
## Great White Egret - Vesper Sparrow nonEst NA NA
## Greater Roadrunner - Heermann's Kangaroo Rat -5.7343 0.2677 Inf
## Greater Roadrunner - Killdeer nonEst NA NA
## Greater Roadrunner - Kit Fox -0.1335 0.3660 Inf
## Greater Roadrunner - Lark Sparrow 0.0000 0.3780 Inf
## Greater Roadrunner - Loggerhead Shrike -0.1335 0.3660 Inf
## Greater Roadrunner - Mohave Ground Squirrel nonEst NA NA
## Greater Roadrunner - Mourning Dove 0.3365 0.4140 Inf
## Greater Roadrunner - Nelson's Antelope Squirrel -3.0099 0.2738 Inf
## Greater Roadrunner - (Red-tailed Hawk) nonEst NA NA
## Greater Roadrunner - Salinas Pocket Mouse nonEst NA NA
## Greater Roadrunner - Vesper Sparrow nonEst NA NA
## Heermann's Kangaroo Rat - Killdeer nonEst NA NA
## Heermann's Kangaroo Rat - Kit Fox 5.6007 0.2505 Inf
## Heermann's Kangaroo Rat - Lark Sparrow 5.7343 0.2677 Inf
## Heermann's Kangaroo Rat - Loggerhead Shrike 5.6007 0.2505 Inf
## Heermann's Kangaroo Rat - Mohave Ground Squirrel nonEst NA NA
## Heermann's Kangaroo Rat - Mourning Dove 6.0707 0.3166 Inf
## Heermann's Kangaroo Rat - Nelson's Antelope Squirrel 2.7243 0.0613 Inf
## Heermann's Kangaroo Rat - (Red-tailed Hawk) nonEst NA NA
## Heermann's Kangaroo Rat - Salinas Pocket Mouse nonEst NA NA
## Heermann's Kangaroo Rat - Vesper Sparrow nonEst NA NA
## Killdeer - Kit Fox nonEst NA NA
## Killdeer - Lark Sparrow nonEst NA NA
## Killdeer - Loggerhead Shrike nonEst NA NA
## Killdeer - Mohave Ground Squirrel nonEst NA NA
## Killdeer - Mourning Dove nonEst NA NA
## Killdeer - Nelson's Antelope Squirrel nonEst NA NA
## Killdeer - (Red-tailed Hawk) nonEst NA NA
## Killdeer - Salinas Pocket Mouse nonEst NA NA
## Killdeer - Vesper Sparrow nonEst NA NA
## Kit Fox - Lark Sparrow 0.1335 0.3660 Inf
## Kit Fox - Loggerhead Shrike 0.0000 0.3536 Inf
## Kit Fox - Mohave Ground Squirrel nonEst NA NA
## Kit Fox - Mourning Dove 0.4700 0.4031 Inf
## Kit Fox - Nelson's Antelope Squirrel -2.8764 0.2569 Inf
## Kit Fox - (Red-tailed Hawk) nonEst NA NA
## Kit Fox - Salinas Pocket Mouse nonEst NA NA
## Kit Fox - Vesper Sparrow nonEst NA NA
## Lark Sparrow - Loggerhead Shrike -0.1335 0.3660 Inf
## Lark Sparrow - Mohave Ground Squirrel nonEst NA NA
## Lark Sparrow - Mourning Dove 0.3365 0.4140 Inf
## Lark Sparrow - Nelson's Antelope Squirrel -3.0099 0.2738 Inf
## Lark Sparrow - (Red-tailed Hawk) nonEst NA NA
## Lark Sparrow - Salinas Pocket Mouse nonEst NA NA
## Lark Sparrow - Vesper Sparrow nonEst NA NA
## Loggerhead Shrike - Mohave Ground Squirrel nonEst NA NA
## Loggerhead Shrike - Mourning Dove 0.4700 0.4031 Inf
## Loggerhead Shrike - Nelson's Antelope Squirrel -2.8764 0.2569 Inf
## Loggerhead Shrike - (Red-tailed Hawk) nonEst NA NA
## Loggerhead Shrike - Salinas Pocket Mouse nonEst NA NA
## Loggerhead Shrike - Vesper Sparrow nonEst NA NA
## Mohave Ground Squirrel - Mourning Dove nonEst NA NA
## Mohave Ground Squirrel - Nelson's Antelope Squirrel nonEst NA NA
## Mohave Ground Squirrel - (Red-tailed Hawk) nonEst NA NA
## Mohave Ground Squirrel - Salinas Pocket Mouse nonEst NA NA
## Mohave Ground Squirrel - Vesper Sparrow nonEst NA NA
## Mourning Dove - Nelson's Antelope Squirrel -3.3464 0.3217 Inf
## Mourning Dove - (Red-tailed Hawk) nonEst NA NA
## Mourning Dove - Salinas Pocket Mouse nonEst NA NA
## Mourning Dove - Vesper Sparrow nonEst NA NA
## Nelson's Antelope Squirrel - (Red-tailed Hawk) nonEst NA NA
## Nelson's Antelope Squirrel - Salinas Pocket Mouse nonEst NA NA
## Nelson's Antelope Squirrel - Vesper Sparrow nonEst NA NA
## (Red-tailed Hawk) - Salinas Pocket Mouse nonEst NA NA
## (Red-tailed Hawk) - Vesper Sparrow nonEst NA NA
## Salinas Pocket Mouse - Vesper Sparrow nonEst NA NA
## z.ratio p.value
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## 10.572 <.0001
## 12.485 <.0001
## 14.127 <.0001
## 0.000 1.0000
## 11.374 <.0001
## 14.931 <.0001
## NA NA
## 13.598 <.0001
## 10.812 <.0001
## 13.233 <.0001
## NA NA
## 13.233 <.0001
## NA NA
## 12.889 <.0001
## -45.721 <.0001
## NA NA
## 13.225 <.0001
## 12.889 <.0001
## 13.225 <.0001
## NA NA
## 11.992 <.0001
## 6.453 <.0001
## NA NA
## NA NA
## NA NA
## -1.386 0.9999
## -3.037 0.3141
## -10.572 <.0001
## -0.533 1.0000
## -4.802 0.0005
## NA NA
## -6.891 <.0001
## -8.167 <.0001
## -7.118 <.0001
## NA NA
## -7.118 <.0001
## NA NA
## -1.736 0.9948
## -16.110 <.0001
## NA NA
## -2.049 0.9559
## -1.736 0.9948
## -2.049 0.9559
## NA NA
## -0.989 1.0000
## -9.350 <.0001
## NA NA
## NA NA
## NA NA
## -1.961 0.9737
## -12.485 <.0001
## 0.888 1.0000
## -4.295 0.0051
## NA NA
## -7.221 <.0001
## -9.042 <.0001
## -7.544 <.0001
## NA NA
## -7.544 <.0001
## NA NA
## -0.392 1.0000
## -20.370 <.0001
## NA NA
## -0.753 1.0000
## -0.392 1.0000
## -0.753 1.0000
## NA NA
## 0.426 1.0000
## -10.736 <.0001
## NA NA
## NA NA
## NA NA
## -14.127 <.0001
## 2.691 0.5827
## -2.773 0.5157
## NA NA
## -6.665 <.0001
## -9.215 <.0001
## -7.112 <.0001
## NA NA
## -7.112 <.0001
## NA NA
## 1.603 0.9985
## -25.381 <.0001
## NA NA
## 1.256 1.0000
## 1.603 0.9985
## 1.256 1.0000
## NA NA
## 2.326 0.8464
## -11.624 <.0001
## NA NA
## NA NA
## NA NA
## 11.374 <.0001
## 14.931 <.0001
## NA NA
## 13.598 <.0001
## 10.812 <.0001
## 13.233 <.0001
## NA NA
## 13.233 <.0001
## NA NA
## 12.889 <.0001
## -45.721 <.0001
## NA NA
## 13.225 <.0001
## 12.889 <.0001
## 13.225 <.0001
## NA NA
## 11.992 <.0001
## 6.453 <.0001
## NA NA
## NA NA
## NA NA
## -4.692 0.0009
## NA NA
## -7.103 <.0001
## -8.584 <.0001
## -7.366 <.0001
## NA NA
## -7.366 <.0001
## NA NA
## -1.263 1.0000
## -17.785 <.0001
## NA NA
## -1.601 0.9985
## -1.263 1.0000
## -1.601 0.9985
## NA NA
## -0.470 1.0000
## -9.957 <.0001
## NA NA
## NA NA
## NA NA
## NA NA
## -4.675 0.0009
## -8.058 <.0001
## -5.254 0.0001
## NA NA
## -5.254 0.0001
## NA NA
## 4.056 0.0135
## -31.020 <.0001
## NA NA
## 3.806 0.0343
## 4.056 0.0135
## 3.806 0.0343
## NA NA
## 4.513 0.0020
## -11.392 <.0001
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## -4.096 0.0115
## -0.665 1.0000
## NA NA
## -0.665 1.0000
## NA NA
## 7.192 <.0001
## -37.890 <.0001
## NA NA
## 7.128 <.0001
## 7.192 <.0001
## 7.128 <.0001
## NA NA
## 7.199 <.0001
## -8.552 <.0001
## NA NA
## NA NA
## NA NA
## 3.463 0.1049
## NA NA
## 3.463 0.1049
## NA NA
## 9.161 <.0001
## -41.731 <.0001
## NA NA
## 9.231 <.0001
## 9.161 <.0001
## 9.231 <.0001
## NA NA
## 8.859 <.0001
## -4.887 0.0003
## NA NA
## NA NA
## NA NA
## NA NA
## 0.000 1.0000
## NA NA
## 7.541 <.0001
## -38.612 <.0001
## NA NA
## 7.500 <.0001
## 7.541 <.0001
## 7.500 <.0001
## NA NA
## 7.494 <.0001
## -8.019 <.0001
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## 7.541 <.0001
## -38.612 <.0001
## NA NA
## 7.500 <.0001
## 7.541 <.0001
## 7.500 <.0001
## NA NA
## 7.494 <.0001
## -8.019 <.0001
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## -21.421 <.0001
## NA NA
## -0.365 1.0000
## 0.000 1.0000
## -0.365 1.0000
## NA NA
## 0.813 1.0000
## -10.994 <.0001
## NA NA
## NA NA
## NA NA
## NA NA
## 22.362 <.0001
## 21.421 <.0001
## 22.362 <.0001
## NA NA
## 19.175 <.0001
## 44.476 <.0001
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## 0.365 1.0000
## 0.000 1.0000
## NA NA
## 1.166 1.0000
## -11.195 <.0001
## NA NA
## NA NA
## NA NA
## -0.365 1.0000
## NA NA
## 0.813 1.0000
## -10.994 <.0001
## NA NA
## NA NA
## NA NA
## NA NA
## 1.166 1.0000
## -11.195 <.0001
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## -10.401 <.0001
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
##
## Results are averaged over the levels of: microsite
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 27 estimatesanimals_density <- photo %>% group_by(site_code,microsite,plot, shrub_density, common_name) %>% summarise(captures = sum(animal.hit))## `summarise()` has grouped output by 'site_code', 'microsite', 'plot',
## 'shrub_density'. You can override using the `.groups` argument.animals_density <- animals_density %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")All current PCOA code for 2022 only
### PCA
library(vegan)## Loading required package: permute## Loading required package: lattice## This is vegan 2.6-4library(ape) ### For PCOA##
## Attaching package: 'ape'## The following object is masked from 'package:ggpubr':
##
## rotate## The following object is masked from 'package:dplyr':
##
## wherepca_data <- animals_density ### Created new df for pca data
pca_data <- pca_data %>%
spread(common_name, captures) %>%
ungroup() %>%
dplyr::select(-site_code, -microsite, -plot) %>%
replace(is.na(.),0)
dim(pca_data)## [1] 22 28env <- read.csv("environment.csv") ### Drop Tecopa open 1, Tecopa open 4, since they have no animal observations.
dim(env)## [1] 22 5m01 <- adonis(pca_data ~ microsite*shrub_density, data = env)## 'adonis' will be deprecated: use 'adonis2' insteadm01## $aov.tab
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## microsite 1 0.2242 0.22425 0.76358 0.03600 0.613
## shrub_density 1 0.4245 0.42452 1.44550 0.06815 0.211
## Residuals 19 5.5799 0.29368 0.89584
## Total 21 6.2287 1.00000
##
## $call
## adonis(formula = pca_data ~ microsite * shrub_density, data = env)
##
## $coefficients
## shrub_density American Robin Black-tailed Jackrabbit
## (Intercept) -4.757692 5.000000e-02 -13.455128
## microsite1 -6.857692 -5.000000e-02 -18.955128
## shrub_density 1.876923 1.823528e-17 4.184615
## microsite1:shrub_density NA NA NA
## Blunt-nosed Leopard Lizard Bobcat
## (Intercept) 0.9987179 -0.6602564
## microsite1 0.8987179 -1.1602564
## shrub_density -0.1538462 0.1692308
## microsite1:shrub_density NA NA
## Brewer's Blackbird California Ground Squirrel
## (Intercept) 0.18717949 20.411538
## microsite1 -0.21282051 13.511538
## shrub_density 0.06153846 -1.815385
## microsite1:shrub_density NA NA
## California Pocket Mouse California Quail
## (Intercept) 0.35641026 -4.2487179
## microsite1 0.15641026 -5.6487179
## shrub_density -0.03076923 0.9538462
## microsite1:shrub_density NA NA
## California Thrasher Common Raven Coyote
## (Intercept) -1.4743590 0.7910256 1.4923077
## microsite1 -1.4743590 -3.5089744 -4.1076923
## shrub_density 0.2769231 0.3230769 0.4769231
## microsite1:shrub_density NA NA NA
## Desert Cottontail Desert Iguana Giant Kangaroo Rat
## (Intercept) -24.729487 2.07820513 6.9935897
## microsite1 -26.629487 -1.82179487 4.4935897
## shrub_density 4.861538 -0.01538462 -0.7692308
## microsite1:shrub_density NA NA NA
## Great White Egret Greater Roadrunner
## (Intercept) 5.000000e-02 -4.3730769
## microsite1 -5.000000e-02 -4.4730769
## shrub_density 1.076807e-17 0.8307692
## microsite1:shrub_density NA NA
## Heermann's Kangaroo Rat Killdeer Kit Fox
## (Intercept) 140.330769 5.000000e-02 0.44487179
## microsite1 59.130769 -5.000000e-02 0.14487179
## shrub_density -7.707692 6.937479e-18 -0.01538462
## microsite1:shrub_density NA NA NA
## Lark Sparrow Loggerhead Shrike Mohave Ground Squirrel
## (Intercept) -1.8384615 -0.5935897 0.81923077
## microsite1 -2.2384615 -0.8935897 0.71923077
## shrub_density 0.3846154 0.1692308 -0.09230769
## microsite1:shrub_density NA NA NA
## Mourning Dove Nelson's Antelope Squirrel
## (Intercept) -1.9384615 18.984615
## microsite1 -2.1384615 13.784615
## shrub_density 0.3846154 -2.246154
## microsite1:shrub_density NA NA
## Red-tailed Hawk Salinas Pocket Mouse Vesper Sparrow
## (Intercept) -0.5641026 5.000000e-02 5.000000e-02
## microsite1 -0.5641026 -5.000000e-02 -5.000000e-02
## shrub_density 0.1076923 1.076807e-17 6.937479e-18
## microsite1:shrub_density NA NA NA
##
## $coef.sites
## 1 2 3 4
## (Intercept) 0.72187241 0.84634032 1.11978420 0.78585052
## microsite1 0.19194029 0.33115230 0.42744428 0.26477687
## shrub_density -0.03063205 -0.05640075 -0.06512504 -0.03711672
## microsite1:shrub_density NA NA NA NA
## 5 6 7 8
## (Intercept) 0.7805666 0.70692759 0.95262164 0.80543797
## microsite1 0.2293742 0.15739580 0.22071669 0.25186788
## shrub_density -0.0393115 -0.02414532 -0.02857079 -0.03727318
## microsite1:shrub_density NA NA NA NA
## 9 10 11 12
## (Intercept) 1.7428122 1.4970441 0.99852391 1.06366871
## microsite1 1.0220992 0.8596173 0.44864549 0.53735308
## shrub_density -0.1847971 -0.1548735 -0.07052619 -0.08656954
## microsite1:shrub_density NA NA NA NA
## 13 14 15 16
## (Intercept) 0.82385406 0.795551518 0.79238103 0.84065136
## microsite1 0.12690322 0.021850057 0.21124835 0.30308675
## shrub_density -0.02568071 -0.008853955 -0.02915258 -0.04325977
## microsite1:shrub_density NA NA NA NA
## 17 18 19 20
## (Intercept) 0.38555800 0.50980072 0.68786923 0.55515661
## microsite1 -0.59216512 -0.43255379 -0.20343288 -0.30788751
## shrub_density 0.09323238 0.06736799 0.02712249 0.04455634
## microsite1:shrub_density NA NA NA NA
## 21 22
## (Intercept) 0.881696341 0.64736295
## microsite1 0.013954406 -0.20302121
## shrub_density -0.001421805 0.02954886
## microsite1:shrub_density NA NA
##
## $f.perms
## [,1] [,2]
## [1,] 1.17523680 1.35296523
## [2,] 0.98166642 0.78004529
## [3,] 0.40043152 1.55001892
## [4,] 0.31585544 0.82015476
## [5,] 1.77201134 3.11595392
## [6,] 1.23814875 2.15375009
## [7,] 0.37267057 0.81109910
## [8,] 1.04940680 0.75474965
## [9,] 1.50863219 1.93679319
## [10,] 0.65908961 1.64324425
## [11,] 0.62490843 0.61053335
## [12,] 1.72810053 2.81913133
## [13,] 0.56427845 1.07807206
## [14,] 0.32257652 0.42267195
## [15,] 0.42170264 0.72860630
## [16,] 1.08603625 1.13489663
## [17,] 1.75691225 0.63314896
## [18,] 0.37031606 1.31843237
## [19,] 0.81095298 2.16315222
## [20,] 2.53012166 1.45869479
## [21,] 1.16418008 0.88624093
## [22,] 0.44793398 0.58256970
## [23,] 1.55247004 0.19280301
## [24,] 0.82778860 1.93641046
## [25,] 1.48826244 3.68241342
## [26,] 0.56990525 1.72230221
## [27,] 0.22126505 1.22268318
## [28,] 0.34594658 1.03154611
## [29,] 2.63897564 1.96627396
## [30,] 0.76933751 0.39674199
## [31,] 0.57609944 1.73548537
## [32,] 1.32117359 1.44365393
## [33,] 0.94641346 0.95836573
## [34,] 0.92627584 3.33801241
## [35,] 1.40526510 1.36674159
## [36,] 2.17450145 0.86843106
## [37,] 0.50154215 1.08691669
## [38,] 1.11395102 0.71755676
## [39,] 0.64133810 0.87063706
## [40,] 0.43348240 0.36842641
## [41,] 1.43259538 1.14958849
## [42,] 1.25117092 0.74648519
## [43,] 0.77958352 0.36754982
## [44,] 0.92504011 1.60079349
## [45,] 0.55793059 0.84906242
## [46,] 1.12795379 1.40404071
## [47,] 0.75102598 0.23198349
## [48,] 0.24981999 1.41797157
## [49,] 1.03430620 1.57316694
## [50,] 1.31815263 0.96738647
## [51,] 0.98454396 0.26428966
## [52,] 0.32770954 0.75141108
## [53,] 0.88856952 1.51162614
## [54,] 0.98978911 0.32633876
## [55,] 1.72449280 1.03682873
## [56,] 0.78350859 0.96128812
## [57,] 0.99896637 0.79036131
## [58,] 1.95992400 1.49567258
## [59,] 1.17256443 0.56493397
## [60,] 1.73408435 1.38771636
## [61,] 2.40040427 0.94766646
## [62,] 0.65001742 1.82871817
## [63,] 0.88626891 1.63947519
## [64,] 1.85930496 1.19317141
## [65,] 0.74873396 1.12989295
## [66,] 0.86150850 1.24418373
## [67,] 0.54840433 2.54703291
## [68,] 0.40323732 0.92932382
## [69,] 1.32357596 1.67204490
## [70,] 0.55850815 0.41373430
## [71,] 0.67077213 1.49705641
## [72,] 0.71988173 0.96679118
## [73,] 1.69939153 1.23519182
## [74,] 0.59986991 0.70684895
## [75,] 0.74801044 0.87525509
## [76,] 0.54157497 0.55964961
## [77,] 1.10588050 1.01637393
## [78,] 0.36061275 0.75608988
## [79,] 0.88592193 1.01024122
## [80,] 0.35030525 0.82622292
## [81,] 0.46448632 1.26086788
## [82,] 0.76056464 0.48575366
## [83,] 1.21124575 0.85114903
## [84,] 2.21803356 1.50961213
## [85,] 0.39878234 0.72572007
## [86,] 1.31642125 0.86826125
## [87,] 1.83692043 0.33226833
## [88,] 0.83305641 0.72073502
## [89,] 1.97551244 2.78762546
## [90,] 0.29906903 0.52789401
## [91,] 0.52455266 0.18369849
## [92,] 0.52828746 0.29119005
## [93,] 0.73460754 0.16090542
## [94,] 0.79779516 0.60638179
## [95,] 1.49942315 0.93383091
## [96,] 0.98669740 1.07460793
## [97,] 1.78769602 1.83056729
## [98,] 0.94901241 2.59566234
## [99,] 1.10579718 1.50049581
## [100,] 0.34039449 1.55964177
## [101,] 0.39380959 1.70449358
## [102,] 0.85243527 0.58430886
## [103,] 2.27098535 1.26212251
## [104,] 1.11178706 0.83923447
## [105,] 1.58612262 1.68470248
## [106,] 0.53677423 0.68768146
## [107,] 0.73353177 1.67882140
## [108,] 2.22572882 0.60095118
## [109,] 0.99709257 0.94447619
## [110,] 0.53319492 0.83939966
## [111,] 2.41463602 0.40208802
## [112,] 0.58081286 0.81602722
## [113,] 0.63422009 0.60187715
## [114,] 1.03709826 0.62547736
## [115,] 1.17566804 0.88287741
## [116,] 0.78230946 0.62917487
## [117,] 1.31134918 1.18608502
## [118,] 0.86420583 1.07268352
## [119,] 1.63913957 1.26762894
## [120,] 0.76676592 0.34208201
## [121,] 0.37769859 0.48225204
## [122,] 1.73149546 0.45368139
## [123,] 0.65904111 1.12316717
## [124,] 0.76141451 0.91132467
## [125,] 0.36690933 0.86673144
## [126,] 2.21094103 0.98405092
## [127,] 0.54713052 1.70549943
## [128,] 1.17991664 1.30055439
## [129,] 0.84915362 1.87307529
## [130,] 6.15329061 0.30255202
## [131,] 0.81973085 0.19647510
## [132,] 0.44148353 0.57954235
## [133,] 0.55221651 1.66479135
## [134,] 0.35168690 0.59607093
## [135,] 0.45864978 0.99585775
## [136,] 0.43473756 0.61437803
## [137,] 0.27427950 0.89825134
## [138,] 1.00771041 1.21801826
## [139,] 0.51640442 1.73028957
## [140,] 1.44850041 0.97569989
## [141,] 1.28025758 0.90976117
## [142,] 1.51208076 1.12374210
## [143,] 1.38468973 1.14830694
## [144,] 0.16580304 0.68701881
## [145,] 1.48523123 0.63146145
## [146,] 0.85952686 0.67717482
## [147,] 0.90346360 1.40588456
## [148,] 0.40960926 0.86961755
## [149,] 0.53942301 0.42404972
## [150,] 2.81302459 0.20193402
## [151,] 1.49024352 1.06039506
## [152,] 0.35287026 1.54308917
## [153,] 0.90770107 1.22737632
## [154,] 5.01693297 0.27677468
## [155,] 2.05752276 0.74942774
## [156,] 0.58195553 1.97370035
## [157,] 2.01794166 0.71379195
## [158,] 1.94994886 1.71222732
## [159,] 0.74160667 0.24215209
## [160,] 1.28831871 1.73011376
## [161,] 2.28482079 1.02781860
## [162,] 1.13813635 1.03585628
## [163,] 2.33044019 1.93813277
## [164,] 1.50164503 1.18109313
## [165,] 0.53605989 1.15618162
## [166,] 0.86263551 0.85288379
## [167,] 0.48129947 1.40481328
## [168,] 0.79215626 1.10017742
## [169,] 0.21298044 0.97004066
## [170,] 0.56225825 3.79992133
## [171,] 0.73520565 0.60639945
## [172,] 1.56164105 1.04826178
## [173,] 0.89501412 0.47155934
## [174,] 1.41717942 0.43967105
## [175,] 0.46508197 1.87725365
## [176,] 0.52007253 1.17855740
## [177,] 1.87272533 1.33145313
## [178,] 0.96115597 0.42851589
## [179,] 0.68814453 0.49398734
## [180,] 0.73862333 1.19968419
## [181,] 1.42414048 0.70469990
## [182,] 1.07609563 0.55795268
## [183,] 1.79718775 0.16113117
## [184,] 0.80853271 1.85703947
## [185,] 0.53878813 0.19631408
## [186,] 0.90432125 3.03457957
## [187,] 1.15039442 0.87475891
## [188,] 0.58582879 1.74347835
## [189,] 0.65728535 3.32398877
## [190,] 1.36358418 1.44692331
## [191,] 1.25123602 0.60940827
## [192,] 1.84734567 0.47638652
## [193,] 1.56749406 0.29766262
## [194,] 0.51697633 0.75896313
## [195,] 0.37915331 1.02776414
## [196,] 0.41635885 1.43760455
## [197,] 0.93319680 0.26067501
## [198,] 0.81190192 1.25877157
## [199,] 1.25909006 0.80491767
## [200,] 0.96257904 1.33618677
## [201,] 1.12477387 1.22156557
## [202,] 1.23028791 2.09505753
## [203,] 0.91438743 1.41261189
## [204,] 0.84338626 0.96498502
## [205,] 0.81912427 1.21415743
## [206,] 1.12777875 0.68984790
## [207,] 1.21881671 0.96833117
## [208,] 0.22845803 0.43374643
## [209,] 0.90603160 0.62657523
## [210,] 0.74676878 0.75838581
## [211,] 1.15406677 0.63395439
## [212,] 0.40570586 1.03063636
## [213,] 1.28251043 1.24998656
## [214,] 0.77914842 1.14648488
## [215,] 0.66271326 0.88415041
## [216,] 1.18353057 0.51893930
## [217,] 1.32689386 0.56857641
## [218,] 1.06828295 0.68132684
## [219,] 0.79433840 0.85349323
## [220,] 0.96374493 0.55768203
## [221,] 0.83610534 0.31109352
## [222,] 1.05040080 0.44337876
## [223,] 0.79389144 0.68863379
## [224,] 0.97117005 1.17646976
## [225,] 2.73388238 0.91129503
## [226,] 0.65423382 1.83973636
## [227,] 0.77495138 1.43598277
## [228,] 0.60914691 0.51248415
## [229,] 0.59242726 0.78418435
## [230,] 1.13975808 0.99329550
## [231,] 0.77905525 1.13669450
## [232,] 0.57237902 0.72361181
## [233,] 0.27856643 1.30751517
## [234,] 0.80859722 1.57513188
## [235,] 0.33319910 1.20255309
## [236,] 1.06104275 0.79553710
## [237,] 1.65761830 1.54580700
## [238,] 2.81717761 0.49874151
## [239,] 0.72116532 1.60560879
## [240,] 1.79373477 0.75183984
## [241,] 1.78794094 1.62531896
## [242,] 1.40499474 0.40937853
## [243,] 0.90682484 0.76557963
## [244,] 0.82456792 0.50682554
## [245,] 1.21038773 2.67295980
## [246,] 0.71262583 0.41892444
## [247,] 0.96837794 0.79538965
## [248,] 0.74828411 0.56511754
## [249,] 0.26613098 1.16471190
## [250,] 0.67416492 1.26677981
## [251,] 1.34772719 0.24940362
## [252,] 1.04944414 1.25607200
## [253,] 0.82298539 2.61308664
## [254,] 1.51169270 1.25994156
## [255,] 1.42520917 0.45462244
## [256,] 1.96511159 1.09253144
## [257,] 1.67490216 0.37807713
## [258,] 1.25703910 0.77974194
## [259,] 0.89221998 0.91252634
## [260,] 1.08470362 0.89530226
## [261,] 0.46255091 1.73872349
## [262,] 0.73871530 0.99868395
## [263,] 2.09512951 0.82889716
## [264,] 0.60616111 0.34044063
## [265,] 0.57517851 0.76724938
## [266,] 0.40252286 0.33131919
## [267,] 0.42210842 0.70401445
## [268,] 0.53617254 1.15595519
## [269,] 0.54430995 0.59134017
## [270,] 0.55009343 0.97988292
## [271,] 1.84143699 0.35328929
## [272,] 0.51514846 0.87282408
## [273,] 1.90134026 1.04269499
## [274,] 0.25809104 0.19685097
## [275,] 0.68932799 0.42169097
## [276,] 0.40300648 0.52426694
## [277,] 2.95665031 0.08391781
## [278,] 0.60936911 0.45384567
## [279,] 1.19775494 2.32101483
## [280,] 0.82058551 0.90197078
## [281,] 0.87736591 0.47963483
## [282,] 1.37134703 0.32417650
## [283,] 0.96802513 1.37089345
## [284,] 1.24438964 2.14989445
## [285,] 1.77221357 0.59720397
## [286,] 0.85675930 0.82530658
## [287,] 0.53313900 0.97310458
## [288,] 1.04663198 1.73825848
## [289,] 0.30282007 0.46098683
## [290,] 0.60999339 0.75248397
## [291,] 0.63877890 1.23344721
## [292,] 1.34243792 2.55781516
## [293,] 0.73298977 0.70657914
## [294,] 0.83435431 1.29269100
## [295,] 1.14791233 1.20189751
## [296,] 1.06621341 1.37702436
## [297,] 0.40692638 2.64764905
## [298,] 0.72193822 0.61662276
## [299,] 1.51687051 0.18042376
## [300,] 0.95001961 0.29626051
## [301,] 1.72433914 0.69503041
## [302,] 0.27077079 0.75961416
## [303,] 0.37079483 1.07612685
## [304,] 1.08956313 1.12735565
## [305,] 1.48228659 2.05160837
## [306,] 0.75381539 1.38784228
## [307,] 0.75893214 0.27046522
## [308,] 1.12775834 1.01355167
## [309,] 0.65910588 1.42214946
## [310,] 1.04160228 1.02802744
## [311,] 1.28544758 2.15868436
## [312,] 1.55679585 3.52030644
## [313,] 0.82082551 0.50298627
## [314,] 0.75175706 0.93680713
## [315,] 0.91914611 1.49142166
## [316,] 1.22773247 1.09243551
## [317,] 1.11308114 1.52185581
## [318,] 0.38358077 0.83192976
## [319,] 1.60040651 2.40158749
## [320,] 1.71480808 0.32851417
## [321,] 0.51616290 1.14166099
## [322,] 0.70433689 0.92485550
## [323,] 0.80410580 2.20331914
## [324,] 1.52168573 0.85819999
## [325,] 0.83199126 0.68466306
## [326,] 0.56912855 1.21941954
## [327,] 0.46324592 0.89747554
## [328,] 0.96736915 0.78498603
## [329,] 1.92614845 0.40884784
## [330,] 0.65419465 0.22047028
## [331,] 1.03694020 1.21182060
## [332,] 3.29054340 1.14038989
## [333,] 0.65962707 0.33033428
## [334,] 0.28955657 1.97965170
## [335,] 1.09577885 0.68652764
## [336,] 1.50890793 1.66133626
## [337,] 1.25254828 1.73150545
## [338,] 0.55620358 0.59109242
## [339,] 0.54949697 0.30124952
## [340,] 1.26580072 0.90850080
## [341,] 0.82348742 1.07597497
## [342,] 0.68702225 1.13277005
## [343,] 0.96111634 1.18428184
## [344,] 1.05228129 0.74196942
## [345,] 0.83546355 0.67597969
## [346,] 1.31615505 1.23081194
## [347,] 2.46225865 1.35143346
## [348,] 0.71533250 0.60874214
## [349,] 0.73692842 0.92060320
## [350,] 0.42139887 1.11293505
## [351,] 0.49389675 0.68761168
## [352,] 1.51963159 0.75208900
## [353,] 0.69726938 0.52497045
## [354,] 0.62720929 1.00837073
## [355,] 2.04995199 1.57162241
## [356,] 1.27347679 1.29361262
## [357,] 0.76274375 1.44103214
## [358,] 1.34194129 1.23486560
## [359,] 0.42688966 1.07077839
## [360,] 0.46044927 4.28560664
## [361,] 0.68039991 0.76846793
## [362,] 0.77976454 1.26575369
## [363,] 0.82146949 0.70420400
## [364,] 0.30430246 0.68201993
## [365,] 1.02866724 0.90858919
## [366,] 1.13432947 1.05469563
## [367,] 0.37746899 1.64290749
## [368,] 1.02803601 0.48021224
## [369,] 0.94199922 0.21488955
## [370,] 1.02729999 0.47015321
## [371,] 0.57517812 0.50682406
## [372,] 0.49609623 0.80525178
## [373,] 2.16867078 1.88172434
## [374,] 0.92880416 3.97503533
## [375,] 0.59180178 0.49299806
## [376,] 1.76229505 1.88483611
## [377,] 1.16659050 2.42951878
## [378,] 1.79854748 0.51035536
## [379,] 0.88770505 0.71943950
## [380,] 0.56819848 0.96741733
## [381,] 0.62016673 1.01421397
## [382,] 0.79774914 0.85866127
## [383,] 0.37697186 0.77271578
## [384,] 1.58123092 3.33532562
## [385,] 0.95772249 0.92062899
## [386,] 1.45576652 1.59903138
## [387,] 0.62533014 0.67185940
## [388,] 0.83106282 0.87355780
## [389,] 1.15832889 0.83753017
## [390,] 2.98982270 2.05321088
## [391,] 0.78620929 1.04365323
## [392,] 1.54075868 2.46208217
## [393,] 0.62366333 0.82309650
## [394,] 1.02335524 1.25159512
## [395,] 0.67273427 2.22766897
## [396,] 0.63416814 0.59617772
## [397,] 0.89315784 0.52481595
## [398,] 0.77453405 1.18665577
## [399,] 0.92699341 0.44545061
## [400,] 1.34444701 0.45523466
## [401,] 0.97100592 1.08883886
## [402,] 0.42124430 0.52996063
## [403,] 0.39156877 0.88542418
## [404,] 1.18968041 1.31822257
## [405,] 0.45389599 0.47211417
## [406,] 1.08460403 0.29781088
## [407,] 1.05310534 0.37018424
## [408,] 0.87674843 1.96902906
## [409,] 1.64426652 1.34441582
## [410,] 2.02181257 0.97080627
## [411,] 0.51453153 0.07748599
## [412,] 0.63527466 1.70081409
## [413,] 1.27477918 0.61713269
## [414,] 0.89101875 0.87222499
## [415,] 0.54975461 0.43430147
## [416,] 0.74860443 0.94844450
## [417,] 0.50643370 3.67574200
## [418,] 1.00735813 0.85849798
## [419,] 1.75829361 1.68146675
## [420,] 0.76232804 0.95701389
## [421,] 1.07494921 2.99489979
## [422,] 0.20498613 1.16574669
## [423,] 1.03105946 0.43391138
## [424,] 0.62723957 2.42055080
## [425,] 0.59015453 0.52122722
## [426,] 1.15902482 0.71940146
## [427,] 0.60657100 1.63655051
## [428,] 0.53022225 1.25136223
## [429,] 1.14132637 0.88576484
## [430,] 0.79710900 1.17735361
## [431,] 0.64926297 0.64768977
## [432,] 0.65463873 0.38542813
## [433,] 1.44905362 0.44109930
## [434,] 0.21957411 0.62605485
## [435,] 0.39795946 0.50775352
## [436,] 1.66600272 0.52058561
## [437,] 0.29678447 1.84805844
## [438,] 1.08302967 0.60357107
## [439,] 1.14360776 2.74326894
## [440,] 2.07746531 0.42175959
## [441,] 0.33066178 0.37912897
## [442,] 2.10599808 1.03836883
## [443,] 0.48655915 0.94393975
## [444,] 1.65884576 0.34967215
## [445,] 1.86990508 1.56730219
## [446,] 0.82601897 0.66569474
## [447,] 0.68206267 0.86747792
## [448,] 0.85860012 0.68573465
## [449,] 0.24114407 0.25064156
## [450,] 1.38196407 1.89708526
## [451,] 0.80223643 0.14407935
## [452,] 0.44656945 1.17383017
## [453,] 1.62566705 0.40384387
## [454,] 0.76618314 0.99336909
## [455,] 1.75808731 0.13769800
## [456,] 0.59789880 1.65839253
## [457,] 1.62747906 0.64521590
## [458,] 1.68155567 0.86393375
## [459,] 0.67728445 0.77032500
## [460,] 1.11680599 1.21806425
## [461,] 0.67236569 0.36999734
## [462,] 0.73310382 0.90123501
## [463,] 1.24148520 1.63207730
## [464,] 0.99439326 0.29472170
## [465,] 1.55631635 2.32379865
## [466,] 0.82680722 1.35890454
## [467,] 0.92328975 0.95816797
## [468,] 0.18421425 1.60838768
## [469,] 0.60512426 0.72839781
## [470,] 0.28812962 1.24210891
## [471,] 0.29209596 0.17606295
## [472,] 2.00975502 1.11300511
## [473,] 0.54707671 0.52718175
## [474,] 1.18787163 1.98260313
## [475,] 0.40171480 1.21873582
## [476,] 1.14139310 1.04639585
## [477,] 3.54195712 0.34914121
## [478,] 0.76637816 1.40076612
## [479,] 1.26981168 0.68104038
## [480,] 1.80881386 2.73974288
## [481,] 1.83245559 2.03945934
## [482,] 0.48682388 0.71140216
## [483,] 0.40165105 1.53682682
## [484,] 1.17446792 0.50477838
## [485,] 1.02633283 1.57352121
## [486,] 0.46856407 0.52551024
## [487,] 0.95515792 0.55646719
## [488,] 0.90012463 0.87045905
## [489,] 1.28605244 0.43625420
## [490,] 1.23523805 0.89008855
## [491,] 0.65078198 1.41471114
## [492,] 0.61057532 0.60223530
## [493,] 1.04959261 2.34786211
## [494,] 0.62060588 0.88166805
## [495,] 1.02080556 0.45586828
## [496,] 0.87526029 0.94724643
## [497,] 2.07904413 0.77797730
## [498,] 0.71041741 0.66228561
## [499,] 1.61097929 0.96726418
## [500,] 0.72569718 0.25624738
## [501,] 1.78026422 1.11704430
## [502,] 0.72544646 2.52691984
## [503,] 2.56602697 0.43464463
## [504,] 0.50583360 0.77332288
## [505,] 1.31753550 1.33470055
## [506,] 0.54737726 1.47258330
## [507,] 2.74801984 0.73153638
## [508,] 0.56568793 0.55888669
## [509,] 0.35421156 1.23260019
## [510,] 1.31176860 0.40441918
## [511,] 1.55616525 0.52981511
## [512,] 2.23841988 1.48739482
## [513,] 1.54127858 0.59273821
## [514,] 0.70052481 0.76990835
## [515,] 0.18170480 0.79628175
## [516,] 0.90962752 1.69838725
## [517,] 2.09454816 1.00983202
## [518,] 0.48130666 1.23562116
## [519,] 0.52084577 0.95478941
## [520,] 0.41555666 1.52869425
## [521,] 0.52290293 1.04121543
## [522,] 1.05622403 2.31463313
## [523,] 0.32841863 0.52698669
## [524,] 1.65967821 2.42933742
## [525,] 1.55552469 0.42456390
## [526,] 1.40433575 1.50810668
## [527,] 0.94681231 0.19913164
## [528,] 0.56703549 0.50379741
## [529,] 0.44284656 0.63411798
## [530,] 0.57239047 0.66079588
## [531,] 1.20449601 0.24918412
## [532,] 2.31528364 2.42043538
## [533,] 1.29501211 0.51812653
## [534,] 0.87514509 3.45588316
## [535,] 0.36180803 0.58716221
## [536,] 0.85171790 0.38650864
## [537,] 0.69688077 1.04712828
## [538,] 2.12695024 0.43287366
## [539,] 0.52600014 0.56944713
## [540,] 1.34602675 0.59635642
## [541,] 1.06791888 1.64563175
## [542,] 0.42196874 0.73596188
## [543,] 1.00126917 0.48329442
## [544,] 0.60663778 1.39973911
## [545,] 1.64880598 0.95452428
## [546,] 1.28246160 1.05952435
## [547,] 0.50719161 1.96481783
## [548,] 0.40404715 0.70780117
## [549,] 0.38662945 0.46032293
## [550,] 1.88699782 0.75351137
## [551,] 0.82060041 1.23814637
## [552,] 0.88932103 1.32306961
## [553,] 0.34740160 1.37850677
## [554,] 0.90049824 0.30899471
## [555,] 1.38021188 3.08812037
## [556,] 2.68743240 0.61357943
## [557,] 1.04592642 0.45472990
## [558,] 0.86834193 0.43553519
## [559,] 0.82859377 0.63924378
## [560,] 1.14786015 2.70693296
## [561,] 0.51392902 1.11518578
## [562,] 0.65543988 1.44095985
## [563,] 1.04068251 1.21603226
## [564,] 0.55815766 0.93361593
## [565,] 0.83522050 0.66375500
## [566,] 0.72518270 0.70083522
## [567,] 1.97730714 1.48353610
## [568,] 0.61339377 0.60230637
## [569,] 1.26272008 0.93715118
## [570,] 1.30927414 0.46964080
## [571,] 1.12699290 0.94772178
## [572,] 1.24344421 0.55605700
## [573,] 1.32496264 0.58104911
## [574,] 0.56507243 0.65296869
## [575,] 0.64703120 1.88770231
## [576,] 1.85990798 1.43062866
## [577,] 0.29135822 0.50589377
## [578,] 0.64986406 1.01164589
## [579,] 0.35484062 0.45316557
## [580,] 1.35023328 1.10382884
## [581,] 2.53154606 0.59371789
## [582,] 1.13872609 0.47022346
## [583,] 0.75917874 1.33239704
## [584,] 0.49838294 0.15700202
## [585,] 0.50866843 0.26193047
## [586,] 3.15409376 1.72169043
## [587,] 0.33734340 1.46991976
## [588,] 0.56501898 0.77091406
## [589,] 0.62293061 0.94963357
## [590,] 1.67331708 0.22716815
## [591,] 1.22901258 1.85624448
## [592,] 0.66320444 1.46761900
## [593,] 0.83659890 0.76134750
## [594,] 0.64221536 1.32681412
## [595,] 0.82885062 1.51809024
## [596,] 0.79190090 0.25342829
## [597,] 0.59775526 1.09395343
## [598,] 0.80836624 1.54318415
## [599,] 0.97715318 0.65017385
## [600,] 1.75701552 1.21016212
## [601,] 0.36316350 0.95085420
## [602,] 1.55683907 2.09960702
## [603,] 2.57388304 1.10716120
## [604,] 1.55458935 0.92726058
## [605,] 0.95762323 0.48343748
## [606,] 1.76582874 0.97266205
## [607,] 1.38883288 0.24989493
## [608,] 0.65538037 0.45296996
## [609,] 0.93617188 0.14064169
## [610,] 2.12102062 0.57995649
## [611,] 0.85948520 0.74929649
## [612,] 0.99999627 0.52258887
## [613,] 1.23820543 1.31490570
## [614,] 0.63924282 1.03050087
## [615,] 0.67133002 2.30115908
## [616,] 0.48265403 0.97662108
## [617,] 0.68073342 0.21136436
## [618,] 0.73977290 0.55999427
## [619,] 0.76800931 0.46928623
## [620,] 0.39535526 1.49939201
## [621,] 1.48679568 0.76102426
## [622,] 0.61721593 1.20272311
## [623,] 1.19699189 1.84908388
## [624,] 0.50274933 1.01049000
## [625,] 0.43793861 0.83525693
## [626,] 1.17200106 2.01748224
## [627,] 1.44000993 3.21440390
## [628,] 0.62039356 1.14388505
## [629,] 1.74483156 1.05502994
## [630,] 2.49570535 1.24569365
## [631,] 1.03912247 0.53174027
## [632,] 0.98495924 1.41736741
## [633,] 1.08827550 0.81431498
## [634,] 0.53490192 0.73678205
## [635,] 0.91127201 0.91874067
## [636,] 1.42407021 0.72220187
## [637,] 1.49694118 1.10970357
## [638,] 0.58757881 0.44649731
## [639,] 2.86295019 0.85863404
## [640,] 0.35851694 0.15974849
## [641,] 1.60288532 0.80306939
## [642,] 1.56176456 2.11813896
## [643,] 0.47495502 0.94881176
## [644,] 0.72028651 0.82429600
## [645,] 0.79139013 1.10597134
## [646,] 0.99965111 1.30483872
## [647,] 0.99625837 1.52340682
## [648,] 0.76310790 0.25793294
## [649,] 0.51070863 2.13162717
## [650,] 1.24360316 0.42017577
## [651,] 0.96870084 1.22298601
## [652,] 2.27885280 1.90898342
## [653,] 1.05019326 2.19135882
## [654,] 1.16427785 1.32716642
## [655,] 0.58942901 0.94165831
## [656,] 1.10582776 1.83418872
## [657,] 0.73623854 0.60238667
## [658,] 0.61709768 1.30065636
## [659,] 0.91720332 0.83020850
## [660,] 0.68311778 1.76149403
## [661,] 0.62515130 0.24972418
## [662,] 1.12948872 1.54780284
## [663,] 0.72948025 2.39477677
## [664,] 1.00363150 1.10519312
## [665,] 0.67785095 0.53700381
## [666,] 1.28071466 1.41545439
## [667,] 1.05884759 0.38852455
## [668,] 1.11559977 0.64507027
## [669,] 1.54020245 0.85886993
## [670,] 1.82098332 2.42888064
## [671,] 0.56625069 0.30361552
## [672,] 0.77208519 0.90583939
## [673,] 1.21402475 1.28865204
## [674,] 0.39641304 0.77621175
## [675,] 3.20023921 0.71468148
## [676,] 0.33110363 0.36552667
## [677,] 0.78569329 0.55287691
## [678,] 1.03472093 1.68791736
## [679,] 0.59981066 0.45145275
## [680,] 1.25372313 0.42804081
## [681,] 0.56130797 0.72349538
## [682,] 0.97805016 0.20056039
## [683,] 1.25755978 1.13303714
## [684,] 1.55662155 1.26429428
## [685,] 0.96482092 1.29290288
## [686,] 1.97217397 1.23239968
## [687,] 0.94561033 0.44491337
## [688,] 1.46275076 1.45408908
## [689,] 1.03040272 0.92012240
## [690,] 0.66894744 0.33972767
## [691,] 0.38996065 1.00943533
## [692,] 1.70445191 2.18953498
## [693,] 0.25890657 1.27993679
## [694,] 1.57510328 0.70771356
## [695,] 0.38976537 1.40515497
## [696,] 0.48630517 0.90819073
## [697,] 0.83565184 1.17059170
## [698,] 0.81911199 0.68688753
## [699,] 1.13914773 0.72122473
## [700,] 0.91774625 0.85794496
## [701,] 1.32802193 0.75795965
## [702,] 0.20423270 0.77550499
## [703,] 0.93960530 0.98696003
## [704,] 0.36276554 0.86878709
## [705,] 0.70620026 0.79261311
## [706,] 0.57574611 1.86468542
## [707,] 1.66142610 0.70539651
## [708,] 0.74860791 0.09798201
## [709,] 0.80474051 1.29801926
## [710,] 1.53280980 0.50637859
## [711,] 1.18312332 1.17360605
## [712,] 0.43002511 0.89132148
## [713,] 2.12352662 3.05631797
## [714,] 0.57980470 0.37759132
## [715,] 1.75552511 0.78894223
## [716,] 0.93259410 3.69322613
## [717,] 0.36282548 1.09037971
## [718,] 1.07618058 1.12717943
## [719,] 0.09873381 1.47479316
## [720,] 0.80639479 1.57846277
## [721,] 0.64202314 0.77588871
## [722,] 1.70593230 0.27166893
## [723,] 0.41079682 1.37298102
## [724,] 1.50887501 0.66015466
## [725,] 0.48972043 0.82139081
## [726,] 0.64363274 0.46518961
## [727,] 1.23352959 0.39086926
## [728,] 0.55701108 1.25317866
## [729,] 0.99842203 0.47665427
## [730,] 1.74016927 1.59237041
## [731,] 1.41894862 2.27528721
## [732,] 1.11569411 0.44252006
## [733,] 0.81052451 0.95353860
## [734,] 0.20214119 0.86748258
## [735,] 1.72941893 0.24300497
## [736,] 0.52574987 0.51454899
## [737,] 1.36674169 1.54263097
## [738,] 1.74963943 0.81953420
## [739,] 0.55730680 0.56058573
## [740,] 1.19032826 0.65053090
## [741,] 0.74447654 1.29622045
## [742,] 0.77593508 2.22806557
## [743,] 0.83900034 0.95949426
## [744,] 0.48967800 0.85122919
## [745,] 0.55341840 1.11601410
## [746,] 0.37952663 0.57181675
## [747,] 0.35991730 1.04841444
## [748,] 0.72398703 1.37951468
## [749,] 2.12874397 2.59414519
## [750,] 1.01949123 0.35903435
## [751,] 1.44901680 2.10592601
## [752,] 1.03045023 1.73698673
## [753,] 0.51249534 0.62207465
## [754,] 1.43239337 0.40037155
## [755,] 0.36635589 0.49800044
## [756,] 0.74636218 1.19642822
## [757,] 0.55851671 0.96172439
## [758,] 2.12598738 0.46058575
## [759,] 0.60522949 1.37722129
## [760,] 0.30529500 0.99534790
## [761,] 0.63965936 0.64070421
## [762,] 0.65619947 0.61842989
## [763,] 1.01091590 0.78601271
## [764,] 1.40355326 1.19551393
## [765,] 1.21564866 0.36679531
## [766,] 0.85409101 0.29028972
## [767,] 1.87726642 1.26043115
## [768,] 1.16393399 0.66497737
## [769,] 0.21856651 1.08196892
## [770,] 1.60006921 2.14273829
## [771,] 1.28916058 0.04489146
## [772,] 1.09335096 0.77220704
## [773,] 2.49754985 1.16411738
## [774,] 0.84308850 0.48355307
## [775,] 0.57610741 0.78298841
## [776,] 0.91598832 2.16507791
## [777,] 1.35549141 1.18090788
## [778,] 1.68925770 0.35580667
## [779,] 1.30420393 0.61024550
## [780,] 0.81978560 0.85083482
## [781,] 0.74751483 1.09551801
## [782,] 0.44970679 1.65396479
## [783,] 0.39228568 0.82957215
## [784,] 0.96871531 0.75036342
## [785,] 0.93696692 0.71354203
## [786,] 1.16799486 1.45788091
## [787,] 1.81254892 0.28249386
## [788,] 0.78958134 1.07662551
## [789,] 0.63510515 0.83158946
## [790,] 1.62191388 0.17540344
## [791,] 0.18880649 1.10094347
## [792,] 0.90434415 2.87640494
## [793,] 2.02162548 2.07006381
## [794,] 0.73154752 2.69325674
## [795,] 0.66898363 3.31618945
## [796,] 1.06760855 1.16962276
## [797,] 1.19640608 0.49451362
## [798,] 2.03093366 0.56595644
## [799,] 0.46572679 1.02368717
## [800,] 1.05214980 0.82502482
## [801,] 0.75721304 1.11724561
## [802,] 0.61732702 0.50423426
## [803,] 0.49308337 0.64500441
## [804,] 1.25408350 1.01195523
## [805,] 0.47457884 0.38967851
## [806,] 1.67329616 1.37858203
## [807,] 0.41814306 1.32049028
## [808,] 1.19552488 1.45442785
## [809,] 0.21608401 1.30952967
## [810,] 0.90863959 1.27530502
## [811,] 0.42266523 0.83902064
## [812,] 0.89582300 1.18130488
## [813,] 1.06570840 0.32106213
## [814,] 0.47422793 0.79915133
## [815,] 0.82334560 1.72449000
## [816,] 2.07993735 2.80873061
## [817,] 1.36532165 1.03099851
## [818,] 0.74559791 0.30496085
## [819,] 1.63101140 1.03084772
## [820,] 1.82890042 0.49539438
## [821,] 0.50167640 1.06949814
## [822,] 1.10820422 0.39755712
## [823,] 3.60113438 1.10231620
## [824,] 0.61046498 0.59464982
## [825,] 0.89618263 0.77303798
## [826,] 0.19425026 0.88279368
## [827,] 1.45581650 0.36851172
## [828,] 1.34865693 1.20910735
## [829,] 0.67997913 1.03510920
## [830,] 0.15753690 1.13586975
## [831,] 0.51815806 5.07060245
## [832,] 0.86197224 1.57676229
## [833,] 0.87016897 1.00143647
## [834,] 2.60792495 0.71987487
## [835,] 0.74851582 2.19897186
## [836,] 0.58227823 1.74104118
## [837,] 2.61944370 0.25804724
## [838,] 1.10536478 0.58490870
## [839,] 1.44789672 0.56159512
## [840,] 1.85146414 0.33434120
## [841,] 4.32862918 0.79598682
## [842,] 0.51356999 0.80932130
## [843,] 0.53675353 2.26169681
## [844,] 0.77506538 0.68400654
## [845,] 2.09512740 2.57188969
## [846,] 1.12813599 0.65725054
## [847,] 0.98388449 0.99286705
## [848,] 1.92977156 0.13183909
## [849,] 1.81233036 0.73670361
## [850,] 1.24013624 0.57978796
## [851,] 1.26103379 2.59185615
## [852,] 0.88960938 0.79299764
## [853,] 1.19369683 1.80585742
## [854,] 0.90108683 0.41462820
## [855,] 1.37684057 0.43769633
## [856,] 0.98586972 1.30939148
## [857,] 1.56996122 1.22117732
## [858,] 4.05339997 0.18547662
## [859,] 0.87797854 1.24297202
## [860,] 0.74156459 0.99431027
## [861,] 1.28273759 0.27834571
## [862,] 1.11699922 1.01669600
## [863,] 0.49927639 1.51761768
## [864,] 0.87149275 0.77291511
## [865,] 0.79636929 1.05217305
## [866,] 0.54446958 1.88297141
## [867,] 1.35669203 1.69329747
## [868,] 0.79139083 1.48211506
## [869,] 0.79306310 1.61663396
## [870,] 0.53617231 0.72554824
## [871,] 1.12053032 1.08157228
## [872,] 0.52372152 0.53295293
## [873,] 1.57528613 0.97943284
## [874,] 0.53746371 1.32676842
## [875,] 1.34409397 0.82262489
## [876,] 0.58456068 0.80628174
## [877,] 2.03928822 1.15999369
## [878,] 0.61796567 0.24895819
## [879,] 0.71766067 1.30729581
## [880,] 1.11078495 1.58943801
## [881,] 1.11081999 0.90947617
## [882,] 1.62957992 1.08105628
## [883,] 0.56514122 0.61113211
## [884,] 0.69475297 1.16688313
## [885,] 0.93984112 1.60451026
## [886,] 0.81540812 1.24650606
## [887,] 1.25182059 0.61782899
## [888,] 0.23958564 0.69658715
## [889,] 1.47641671 1.10066063
## [890,] 0.64709202 1.45807253
## [891,] 1.51184358 1.15058309
## [892,] 2.49067817 1.79425208
## [893,] 1.40989005 0.37502444
## [894,] 0.80973226 1.31310551
## [895,] 1.44701571 1.09180800
## [896,] 1.94094805 1.29957325
## [897,] 1.85784339 0.70181365
## [898,] 0.83709056 0.70940252
## [899,] 0.31536371 2.28069925
## [900,] 0.25900435 1.93227606
## [901,] 0.40748051 0.91489648
## [902,] 0.65660895 1.12692690
## [903,] 0.82204061 1.03226988
## [904,] 1.32184354 1.24839304
## [905,] 0.79338500 0.67727147
## [906,] 0.66799530 1.32882336
## [907,] 0.97292020 1.31288996
## [908,] 1.48780221 1.81997846
## [909,] 0.62510881 0.77756576
## [910,] 0.46974454 0.48425767
## [911,] 1.96184752 0.76192068
## [912,] 0.73904997 2.04766600
## [913,] 1.13426238 1.14507762
## [914,] 0.70463026 1.05401694
## [915,] 0.67563564 1.51736256
## [916,] 1.31475657 2.20707052
## [917,] 0.17390875 1.36192766
## [918,] 1.48057759 0.92394053
## [919,] 0.42456894 0.85636976
## [920,] 2.27625767 1.26432540
## [921,] 0.87810189 0.67755399
## [922,] 1.06752545 1.06696469
## [923,] 1.80887284 1.01632172
## [924,] 1.16562092 0.85451967
## [925,] 1.92696983 0.37374084
## [926,] 1.34978578 0.96156596
## [927,] 0.48427818 2.19574435
## [928,] 1.14551138 2.08147284
## [929,] 0.75736130 1.11200852
## [930,] 1.19181157 0.11466078
## [931,] 1.32423546 1.11946874
## [932,] 0.78496369 0.87508383
## [933,] 1.30178089 1.05190178
## [934,] 0.98277570 0.57448775
## [935,] 1.25859957 0.97205832
## [936,] 1.86790922 0.68754858
## [937,] 1.29904495 1.01102824
## [938,] 1.26762078 0.87082992
## [939,] 0.35466133 0.20543129
## [940,] 0.49666318 3.45863482
## [941,] 0.92049659 1.85276146
## [942,] 1.18822034 1.64785113
## [943,] 1.40636548 2.45177260
## [944,] 0.77353591 1.25695198
## [945,] 0.95456539 0.53531649
## [946,] 1.35773434 3.00796492
## [947,] 0.75712650 0.60279750
## [948,] 1.16892249 1.21549117
## [949,] 0.69261752 1.77061875
## [950,] 0.62159658 1.03859306
## [951,] 0.33106356 0.70290055
## [952,] 0.39378655 0.31428341
## [953,] 1.03706635 0.97503533
## [954,] 0.77602159 0.51656912
## [955,] 0.72052906 0.82563454
## [956,] 0.63128675 1.81375383
## [957,] 0.68872843 0.73182739
## [958,] 0.75561275 0.23251083
## [959,] 1.20591225 0.59770677
## [960,] 1.58567718 1.15259835
## [961,] 0.53854870 0.64593568
## [962,] 0.41537872 0.64637812
## [963,] 0.78061421 1.51401713
## [964,] 0.18036516 2.13342315
## [965,] 0.82719421 1.11190985
## [966,] 0.24018955 0.99668062
## [967,] 0.41488357 1.30866447
## [968,] 1.64962595 0.97874453
## [969,] 1.46045392 0.44362278
## [970,] 0.66378657 0.68798715
## [971,] 2.68774740 0.25696372
## [972,] 1.68862370 0.70656443
## [973,] 0.88655645 0.56525996
## [974,] 1.04970407 0.49649119
## [975,] 0.27050448 0.93403399
## [976,] 0.91734601 0.49203899
## [977,] 1.90310819 1.05225374
## [978,] 0.18139904 0.96296166
## [979,] 1.10237024 0.50461132
## [980,] 0.86342220 1.47842263
## [981,] 1.03320975 0.51547367
## [982,] 0.50731128 1.06394333
## [983,] 1.18967425 1.02081122
## [984,] 1.83377541 0.99103334
## [985,] 2.61949013 1.45993651
## [986,] 1.01351881 1.03241760
## [987,] 1.11880029 1.04859155
## [988,] 0.36926907 1.44595535
## [989,] 0.23511625 1.45019684
## [990,] 1.49239145 0.50904018
## [991,] 0.79617557 2.58323579
## [992,] 0.92473477 0.50651023
## [993,] 0.80655065 1.11536448
## [994,] 0.85752977 1.04795680
## [995,] 1.37434670 0.75244726
## [996,] 0.80631858 0.36351018
## [997,] 0.66350688 0.41710199
## [998,] 0.45220216 1.93384597
## [999,] 1.42647962 1.48353223
##
## $model.matrix
## (Intercept) microsite1 shrub_density
## 1 1 1 11
## 2 1 1 12
## 3 1 -1 0
## 4 1 -1 0
## 5 1 1 11
## 6 1 1 10
## 7 1 -1 0
## 8 1 -1 0
## 9 1 1 14
## 10 1 1 13
## 11 1 -1 0
## 12 1 -1 0
## 13 1 1 11
## 14 1 1 11
## 15 1 -1 0
## 16 1 -1 0
## 17 1 1 10
## 18 1 1 11
## 19 1 1 11
## 20 1 1 10
## 21 1 -1 0
## 22 1 -1 0
##
## $terms
## pca_data ~ microsite * shrub_density
## attr(,"variables")
## list(pca_data, microsite, shrub_density)
## attr(,"factors")
## microsite shrub_density microsite:shrub_density
## pca_data 0 0 0
## microsite 1 0 1
## shrub_density 0 1 1
## attr(,"term.labels")
## [1] "microsite" "shrub_density"
## [3] "microsite:shrub_density"
## attr(,"order")
## [1] 1 1 2
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
##
## attr(,"class")
## [1] "adonis"dist <- vegdist(pca_data, species = "bray")
res <- pcoa(dist)
p1 <- as.data.frame(res$vectors)%>%
dplyr::select(Axis.1, Axis.2) %>%
bind_cols(env,.)
ggplot(p1, aes(Axis.1, Axis.2, group = microsite)) +
geom_point(aes(color = microsite)) +
geom_text(aes(label=plot), hjust = 0, vjust = 0, check_overlap = TRUE, nudge_x = 0.01)+
scale_color_brewer(palette = "Set1") +
labs(color = "", subtitle = "labels denote plot identity")
m02 <- betadisper(dist, env$microsite)
m02##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist, group = env$microsite)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## Density Open
## 0.5063 0.4721
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 21 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.4339 1.1869 0.9571 0.4725 0.4027 0.3112 0.1809 0.1156anova(m02)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 1 0.00638 0.006378 0.1212 0.7313
## Residuals 20 1.05203 0.052602permutest(m02,pairwise = TRUE, permutations = 99)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.00638 0.006378 0.1212 99 0.71
## Residuals 20 1.05203 0.052602
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Density Open
## Density 0.7
## Open 0.73133m02.HSD <- TukeyHSD(m02)
boxplot(m02)
m03 <- betadisper(dist, env$shrub_density)
m03##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist, group = env$shrub_density)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## 0 10 11 12 13 14
## 0.4721 0.5171 0.4968 0.0000 0.0000 0.0000
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 21 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.4339 1.1869 0.9571 0.4725 0.4027 0.3112 0.1809 0.1156anova(m03)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 5 0.61999 0.12400 2.3507 0.08827 .
## Residuals 16 0.84400 0.05275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1permutest(m03,pairwise = TRUE, permutations = 99)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 5 0.61999 0.12400 2.3507 99 0.06 .
## Residuals 16 0.84400 0.05275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## 0 10 11 12 13 14
## 0 0.71000 0.85000
## 10 0.76968 0.87000
## 11 0.84659 0.89212
## 12
## 13
## 14m03.HSD <- TukeyHSD(m03)
boxplot(m03)
m04 <- betadisper(dist, env$site)
m04##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist, group = env$site)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## Carrizo Cuyama Tecopa
## 0.2828 0.3427 0.4625
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 21 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.4339 1.1869 0.9571 0.4725 0.4027 0.3112 0.1809 0.1156anova(m04)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 2 0.11217 0.056087 1.1941 0.3247
## Residuals 19 0.89240 0.046969permutest(m04,pairwise = TRUE, permutations = 99)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 2 0.11217 0.056087 1.1941 99 0.29
## Residuals 19 0.89240 0.046969
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Carrizo Cuyama Tecopa
## Carrizo 0.52000 0.15
## Cuyama 0.54695 0.34
## Tecopa 0.19772 0.31784m04.HSD <- TukeyHSD(m04)
boxplot(m04)
### 2023 Data
photo_2023 <- read.csv("observations_2023.csv")
summary(photo_2023)## region site site_code microsite
## Length:192701 Length:192701 Length:192701 Length:192701
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## plot cam_ID month date
## Min. :1.000 Min. :1.00 Length:192701 Length:192701
## 1st Qu.:1.000 1st Qu.:1.00 Class :character Class :character
## Median :2.000 Median :1.00 Mode :character Mode :character
## Mean :1.771 Mean :1.49
## 3rd Qu.:2.000 3rd Qu.:2.00
## Max. :4.000 Max. :2.00
## year shrub_density rep identified_by
## Min. :2023 Min. : 0.000 Min. : 1 Length:192701
## 1st Qu.:2023 1st Qu.: 0.000 1st Qu.: 48176 Class :character
## Median :2023 Median :10.000 Median : 96351 Mode :character
## Mean :2023 Mean : 6.239 Mean : 96351
## 3rd Qu.:2023 3rd Qu.:11.000 3rd Qu.:144526
## Max. :2023 Max. :14.000 Max. :192701
## filename timestamp animal.hit class
## Length:192701 Length:192701 Min. :0.000000 Length:192701
## Class :character Class :character 1st Qu.:0.000000 Class :character
## Mode :character Mode :character Median :0.000000 Mode :character
## Mean :0.005392
## 3rd Qu.:0.000000
## Max. :1.000000
## order family genus species
## Length:192701 Length:192701 Length:192701 Length:192701
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## common_name number_of_objects
## Length:192701 Min. :1
## Class :character 1st Qu.:1
## Mode :character Median :1
## Mean :1
## 3rd Qu.:1
## Max. :2photo_2023 <- photo_2023 %>%
filter(common_name != "Human")
photo_2023 <- photo_2023 %>%
filter(common_name != "Human-Camera Trapper")
photo_2023 <- photo_2023 %>%
filter(common_name != "Domestic Dog")
photo_2023 <- photo_2023 %>%
filter(common_name != "Vehicle")
photo_2023 <- photo_2023 %>%
dplyr::filter(common_name != "Insect")
photo_2023 <- photo_2023 %>%
dplyr::filter(common_name != "Animal")
photo_2023 <- photo_2023 %>%
dplyr::filter(common_name != "Bird")
photo_2023 <- photo_2023 %>%
dplyr::filter(common_name != "No CV Result")
count.hit_2023 <- photo_2023 %>%
count(animal.hit) %>%
na.omit()
summary(count.hit_2023)## animal.hit n
## Min. :0.00 Min. : 546
## 1st Qu.:0.25 1st Qu.: 48325
## Median :0.50 Median : 96104
## Mean :0.50 Mean : 96104
## 3rd Qu.:0.75 3rd Qu.:143883
## Max. :1.00 Max. :191662### 2023 had a 0.28% capture rate### Animal Observations by Site_Code
animals_by_sitecode_2023 <- photo_2023%>%
group_by(site_code, microsite, common_name) %>%
summarise(captures = sum(animal.hit), n = n())## `summarise()` has grouped output by 'site_code', 'microsite'. You can override
## using the `.groups` argument.animals_by_sitecode_2023 <- animals_by_sitecode_2023 %>%
filter(common_name != "Blank") %>% filter(common_name != "No CV Result") ### Animal observations by Site 2023
animals_by_site_2023 <- photo_2023 %>% group_by(site,microsite,common_name) %>% summarise(captures = sum(animal.hit))## `summarise()` has grouped output by 'site', 'microsite'. You can override using
## the `.groups` argument.animals_by_site_2023 <- animals_by_site_2023 %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result") ### Animal observations by Density
animals_by_density_2023 <- photo_2023 %>% group_by(microsite,common_name) %>% summarise(captures = sum(animal.hit))## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.animals_by_density_2023 <- animals_by_density_2023 %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")### Total Observations 2023
Total_Observations_2023 <- photo_2023 %>% group_by(common_name) %>% summarise(total = sum(animal.hit)) %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")density_obvs_2023 <- merge(animals_by_density_2023, Total_Observations_2023, all = TRUE)
density_obvs_2023$percent_presence <- density_obvs_2023$captures/density_obvs_2023$total### Percent proportion Figure
plot2 <- ggplot(density_obvs_2023, aes(common_name, percent_presence, fill = microsite)) + geom_bar(stat = "identity") + coord_flip() + theme_classic() + scale_x_discrete(limits=rev) + xlab("Species") + ylab("Percent Proportion") + labs(fill = "Microsite")
plot2 + scale_fill_manual(values = c("#009900", "#0066cc"))
m2<- glm(total ~ microsite*common_name, family = "poisson", data = density_obvs_2023)
anova(m2, test = "Chisq")## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: total
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 37 2413.4
## microsite 1 89.8 36 2323.6 <2e-16 ***
## common_name 24 2323.6 12 0.0 <2e-16 ***
## microsite:common_name 12 0.0 0 0.0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e2 <- emmeans(m2, pairwise~common_name)## NOTE: Results may be misleading due to involvement in interactionse2## $emmeans
## common_name emmean SE df asymp.LCL asymp.UCL
## Black-tailed Jackrabbit 4.84 0.0630 Inf 4.713 4.96
## Black-throated Sparrow nonEst NA NA NA NA
## Blunt-nosed Leopard Lizard nonEst NA NA NA NA
## Brewer's Blackbird 1.61 0.3162 Inf 0.990 2.23
## California Ground Squirrel nonEst NA NA NA NA
## California Quail nonEst NA NA NA NA
## California Thrasher nonEst NA NA NA NA
## Common Raven 3.30 0.1361 Inf 3.029 3.56
## Coyote 3.81 0.1054 Inf 3.600 4.01
## Desert Cottontail nonEst NA NA NA NA
## Desert Iguana nonEst NA NA NA NA
## Giant Kangaroo Rat 2.20 0.2357 Inf 1.735 2.66
## Heermann's Kangaroo Rat 5.45 0.0464 Inf 5.356 5.54
## Horned Lark nonEst NA NA NA NA
## Kit Fox 1.95 0.2673 Inf 1.422 2.47
## Lizards and Snakes nonEst NA NA NA NA
## Loggerhead Shrike 1.61 0.3162 Inf 0.990 2.23
## Mammal nonEst NA NA NA NA
## Merriam's Kangaroo Rat 2.56 0.1961 Inf 2.181 2.95
## Nelson's Antelope Squirrel 3.37 0.1313 Inf 3.110 3.62
## Salinas Pocket Mouse 1.10 0.4082 Inf 0.298 1.90
## Say's Phoebe 1.39 0.3536 Inf 0.693 2.08
## Vesper Sparrow nonEst NA NA NA NA
## Western whiptail nonEst NA NA NA NA
## White-tailed Antelope Squirrel 1.79 0.2887 Inf 1.226 2.36
##
## Results are averaged over the levels of: microsite
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate
## (Black-tailed Jackrabbit) - (Black-throated Sparrow) nonEst
## (Black-tailed Jackrabbit) - (Blunt-nosed Leopard Lizard) nonEst
## (Black-tailed Jackrabbit) - Brewer's Blackbird 3.2268
## (Black-tailed Jackrabbit) - California Ground Squirrel nonEst
## (Black-tailed Jackrabbit) - California Quail nonEst
## (Black-tailed Jackrabbit) - California Thrasher nonEst
## (Black-tailed Jackrabbit) - Common Raven 1.5404
## (Black-tailed Jackrabbit) - Coyote 1.0296
## (Black-tailed Jackrabbit) - Desert Cottontail nonEst
## (Black-tailed Jackrabbit) - Desert Iguana nonEst
## (Black-tailed Jackrabbit) - Giant Kangaroo Rat 2.6391
## (Black-tailed Jackrabbit) - Heermann's Kangaroo Rat -0.6105
## (Black-tailed Jackrabbit) - Horned Lark nonEst
## (Black-tailed Jackrabbit) - Kit Fox 2.8904
## (Black-tailed Jackrabbit) - Lizards and Snakes nonEst
## (Black-tailed Jackrabbit) - Loggerhead Shrike 3.2268
## (Black-tailed Jackrabbit) - Mammal nonEst
## (Black-tailed Jackrabbit) - Merriam's Kangaroo Rat 2.2713
## (Black-tailed Jackrabbit) - Nelson's Antelope Squirrel 1.4690
## (Black-tailed Jackrabbit) - Salinas Pocket Mouse 3.7377
## (Black-tailed Jackrabbit) - Say's Phoebe 3.4500
## (Black-tailed Jackrabbit) - Vesper Sparrow nonEst
## (Black-tailed Jackrabbit) - Western whiptail nonEst
## (Black-tailed Jackrabbit) - (White-tailed Antelope Squirrel) 3.0445
## (Black-throated Sparrow) - (Blunt-nosed Leopard Lizard) nonEst
## (Black-throated Sparrow) - Brewer's Blackbird nonEst
## (Black-throated Sparrow) - California Ground Squirrel nonEst
## (Black-throated Sparrow) - California Quail nonEst
## (Black-throated Sparrow) - California Thrasher nonEst
## (Black-throated Sparrow) - Common Raven nonEst
## (Black-throated Sparrow) - Coyote nonEst
## (Black-throated Sparrow) - Desert Cottontail nonEst
## (Black-throated Sparrow) - Desert Iguana nonEst
## (Black-throated Sparrow) - Giant Kangaroo Rat nonEst
## (Black-throated Sparrow) - Heermann's Kangaroo Rat nonEst
## (Black-throated Sparrow) - Horned Lark nonEst
## (Black-throated Sparrow) - Kit Fox nonEst
## (Black-throated Sparrow) - Lizards and Snakes nonEst
## (Black-throated Sparrow) - Loggerhead Shrike nonEst
## (Black-throated Sparrow) - Mammal nonEst
## (Black-throated Sparrow) - Merriam's Kangaroo Rat nonEst
## (Black-throated Sparrow) - Nelson's Antelope Squirrel nonEst
## (Black-throated Sparrow) - Salinas Pocket Mouse nonEst
## (Black-throated Sparrow) - Say's Phoebe nonEst
## (Black-throated Sparrow) - Vesper Sparrow nonEst
## (Black-throated Sparrow) - Western whiptail nonEst
## (Black-throated Sparrow) - (White-tailed Antelope Squirrel) nonEst
## (Blunt-nosed Leopard Lizard) - Brewer's Blackbird nonEst
## (Blunt-nosed Leopard Lizard) - California Ground Squirrel nonEst
## (Blunt-nosed Leopard Lizard) - California Quail nonEst
## (Blunt-nosed Leopard Lizard) - California Thrasher nonEst
## (Blunt-nosed Leopard Lizard) - Common Raven nonEst
## (Blunt-nosed Leopard Lizard) - Coyote nonEst
## (Blunt-nosed Leopard Lizard) - Desert Cottontail nonEst
## (Blunt-nosed Leopard Lizard) - Desert Iguana nonEst
## (Blunt-nosed Leopard Lizard) - Giant Kangaroo Rat nonEst
## (Blunt-nosed Leopard Lizard) - Heermann's Kangaroo Rat nonEst
## (Blunt-nosed Leopard Lizard) - Horned Lark nonEst
## (Blunt-nosed Leopard Lizard) - Kit Fox nonEst
## (Blunt-nosed Leopard Lizard) - Lizards and Snakes nonEst
## (Blunt-nosed Leopard Lizard) - Loggerhead Shrike nonEst
## (Blunt-nosed Leopard Lizard) - Mammal nonEst
## (Blunt-nosed Leopard Lizard) - Merriam's Kangaroo Rat nonEst
## (Blunt-nosed Leopard Lizard) - Nelson's Antelope Squirrel nonEst
## (Blunt-nosed Leopard Lizard) - Salinas Pocket Mouse nonEst
## (Blunt-nosed Leopard Lizard) - Say's Phoebe nonEst
## (Blunt-nosed Leopard Lizard) - Vesper Sparrow nonEst
## (Blunt-nosed Leopard Lizard) - Western whiptail nonEst
## (Blunt-nosed Leopard Lizard) - (White-tailed Antelope Squirrel) nonEst
## Brewer's Blackbird - California Ground Squirrel nonEst
## Brewer's Blackbird - California Quail nonEst
## Brewer's Blackbird - California Thrasher nonEst
## Brewer's Blackbird - Common Raven -1.6864
## Brewer's Blackbird - Coyote -2.1972
## Brewer's Blackbird - Desert Cottontail nonEst
## Brewer's Blackbird - Desert Iguana nonEst
## Brewer's Blackbird - Giant Kangaroo Rat -0.5878
## Brewer's Blackbird - Heermann's Kangaroo Rat -3.8373
## Brewer's Blackbird - Horned Lark nonEst
## Brewer's Blackbird - Kit Fox -0.3365
## Brewer's Blackbird - Lizards and Snakes nonEst
## Brewer's Blackbird - Loggerhead Shrike 0.0000
## Brewer's Blackbird - Mammal nonEst
## Brewer's Blackbird - Merriam's Kangaroo Rat -0.9555
## Brewer's Blackbird - Nelson's Antelope Squirrel -1.7579
## Brewer's Blackbird - Salinas Pocket Mouse 0.5108
## Brewer's Blackbird - Say's Phoebe 0.2231
## Brewer's Blackbird - Vesper Sparrow nonEst
## Brewer's Blackbird - Western whiptail nonEst
## Brewer's Blackbird - (White-tailed Antelope Squirrel) -0.1823
## California Ground Squirrel - California Quail nonEst
## California Ground Squirrel - California Thrasher nonEst
## California Ground Squirrel - Common Raven nonEst
## California Ground Squirrel - Coyote nonEst
## California Ground Squirrel - Desert Cottontail nonEst
## California Ground Squirrel - Desert Iguana nonEst
## California Ground Squirrel - Giant Kangaroo Rat nonEst
## California Ground Squirrel - Heermann's Kangaroo Rat nonEst
## California Ground Squirrel - Horned Lark nonEst
## California Ground Squirrel - Kit Fox nonEst
## California Ground Squirrel - Lizards and Snakes nonEst
## California Ground Squirrel - Loggerhead Shrike nonEst
## California Ground Squirrel - Mammal nonEst
## California Ground Squirrel - Merriam's Kangaroo Rat nonEst
## California Ground Squirrel - Nelson's Antelope Squirrel nonEst
## California Ground Squirrel - Salinas Pocket Mouse nonEst
## California Ground Squirrel - Say's Phoebe nonEst
## California Ground Squirrel - Vesper Sparrow nonEst
## California Ground Squirrel - Western whiptail nonEst
## California Ground Squirrel - (White-tailed Antelope Squirrel) nonEst
## California Quail - California Thrasher nonEst
## California Quail - Common Raven nonEst
## California Quail - Coyote nonEst
## California Quail - Desert Cottontail nonEst
## California Quail - Desert Iguana nonEst
## California Quail - Giant Kangaroo Rat nonEst
## California Quail - Heermann's Kangaroo Rat nonEst
## California Quail - Horned Lark nonEst
## California Quail - Kit Fox nonEst
## California Quail - Lizards and Snakes nonEst
## California Quail - Loggerhead Shrike nonEst
## California Quail - Mammal nonEst
## California Quail - Merriam's Kangaroo Rat nonEst
## California Quail - Nelson's Antelope Squirrel nonEst
## California Quail - Salinas Pocket Mouse nonEst
## California Quail - Say's Phoebe nonEst
## California Quail - Vesper Sparrow nonEst
## California Quail - Western whiptail nonEst
## California Quail - (White-tailed Antelope Squirrel) nonEst
## California Thrasher - Common Raven nonEst
## California Thrasher - Coyote nonEst
## California Thrasher - Desert Cottontail nonEst
## California Thrasher - Desert Iguana nonEst
## California Thrasher - Giant Kangaroo Rat nonEst
## California Thrasher - Heermann's Kangaroo Rat nonEst
## California Thrasher - Horned Lark nonEst
## California Thrasher - Kit Fox nonEst
## California Thrasher - Lizards and Snakes nonEst
## California Thrasher - Loggerhead Shrike nonEst
## California Thrasher - Mammal nonEst
## California Thrasher - Merriam's Kangaroo Rat nonEst
## California Thrasher - Nelson's Antelope Squirrel nonEst
## California Thrasher - Salinas Pocket Mouse nonEst
## California Thrasher - Say's Phoebe nonEst
## California Thrasher - Vesper Sparrow nonEst
## California Thrasher - Western whiptail nonEst
## California Thrasher - (White-tailed Antelope Squirrel) nonEst
## Common Raven - Coyote -0.5108
## Common Raven - Desert Cottontail nonEst
## Common Raven - Desert Iguana nonEst
## Common Raven - Giant Kangaroo Rat 1.0986
## Common Raven - Heermann's Kangaroo Rat -2.1509
## Common Raven - Horned Lark nonEst
## Common Raven - Kit Fox 1.3499
## Common Raven - Lizards and Snakes nonEst
## Common Raven - Loggerhead Shrike 1.6864
## Common Raven - Mammal nonEst
## Common Raven - Merriam's Kangaroo Rat 0.7309
## Common Raven - Nelson's Antelope Squirrel -0.0715
## Common Raven - Salinas Pocket Mouse 2.1972
## Common Raven - Say's Phoebe 1.9095
## Common Raven - Vesper Sparrow nonEst
## Common Raven - Western whiptail nonEst
## Common Raven - (White-tailed Antelope Squirrel) 1.5041
## Coyote - Desert Cottontail nonEst
## Coyote - Desert Iguana nonEst
## Coyote - Giant Kangaroo Rat 1.6094
## Coyote - Heermann's Kangaroo Rat -1.6401
## Coyote - Horned Lark nonEst
## Coyote - Kit Fox 1.8608
## Coyote - Lizards and Snakes nonEst
## Coyote - Loggerhead Shrike 2.1972
## Coyote - Mammal nonEst
## Coyote - Merriam's Kangaroo Rat 1.2417
## Coyote - Nelson's Antelope Squirrel 0.4394
## Coyote - Salinas Pocket Mouse 2.7081
## Coyote - Say's Phoebe 2.4204
## Coyote - Vesper Sparrow nonEst
## Coyote - Western whiptail nonEst
## Coyote - (White-tailed Antelope Squirrel) 2.0149
## Desert Cottontail - Desert Iguana nonEst
## Desert Cottontail - Giant Kangaroo Rat nonEst
## Desert Cottontail - Heermann's Kangaroo Rat nonEst
## Desert Cottontail - Horned Lark nonEst
## Desert Cottontail - Kit Fox nonEst
## Desert Cottontail - Lizards and Snakes nonEst
## Desert Cottontail - Loggerhead Shrike nonEst
## Desert Cottontail - Mammal nonEst
## Desert Cottontail - Merriam's Kangaroo Rat nonEst
## Desert Cottontail - Nelson's Antelope Squirrel nonEst
## Desert Cottontail - Salinas Pocket Mouse nonEst
## Desert Cottontail - Say's Phoebe nonEst
## Desert Cottontail - Vesper Sparrow nonEst
## Desert Cottontail - Western whiptail nonEst
## Desert Cottontail - (White-tailed Antelope Squirrel) nonEst
## Desert Iguana - Giant Kangaroo Rat nonEst
## Desert Iguana - Heermann's Kangaroo Rat nonEst
## Desert Iguana - Horned Lark nonEst
## Desert Iguana - Kit Fox nonEst
## Desert Iguana - Lizards and Snakes nonEst
## Desert Iguana - Loggerhead Shrike nonEst
## Desert Iguana - Mammal nonEst
## Desert Iguana - Merriam's Kangaroo Rat nonEst
## Desert Iguana - Nelson's Antelope Squirrel nonEst
## Desert Iguana - Salinas Pocket Mouse nonEst
## Desert Iguana - Say's Phoebe nonEst
## Desert Iguana - Vesper Sparrow nonEst
## Desert Iguana - Western whiptail nonEst
## Desert Iguana - (White-tailed Antelope Squirrel) nonEst
## Giant Kangaroo Rat - Heermann's Kangaroo Rat -3.2495
## Giant Kangaroo Rat - Horned Lark nonEst
## Giant Kangaroo Rat - Kit Fox 0.2513
## Giant Kangaroo Rat - Lizards and Snakes nonEst
## Giant Kangaroo Rat - Loggerhead Shrike 0.5878
## Giant Kangaroo Rat - Mammal nonEst
## Giant Kangaroo Rat - Merriam's Kangaroo Rat -0.3677
## Giant Kangaroo Rat - Nelson's Antelope Squirrel -1.1701
## Giant Kangaroo Rat - Salinas Pocket Mouse 1.0986
## Giant Kangaroo Rat - Say's Phoebe 0.8109
## Giant Kangaroo Rat - Vesper Sparrow nonEst
## Giant Kangaroo Rat - Western whiptail nonEst
## Giant Kangaroo Rat - (White-tailed Antelope Squirrel) 0.4055
## Heermann's Kangaroo Rat - Horned Lark nonEst
## Heermann's Kangaroo Rat - Kit Fox 3.5008
## Heermann's Kangaroo Rat - Lizards and Snakes nonEst
## Heermann's Kangaroo Rat - Loggerhead Shrike 3.8373
## Heermann's Kangaroo Rat - Mammal nonEst
## Heermann's Kangaroo Rat - Merriam's Kangaroo Rat 2.8818
## Heermann's Kangaroo Rat - Nelson's Antelope Squirrel 2.0794
## Heermann's Kangaroo Rat - Salinas Pocket Mouse 4.3481
## Heermann's Kangaroo Rat - Say's Phoebe 4.0604
## Heermann's Kangaroo Rat - Vesper Sparrow nonEst
## Heermann's Kangaroo Rat - Western whiptail nonEst
## Heermann's Kangaroo Rat - (White-tailed Antelope Squirrel) 3.6550
## Horned Lark - Kit Fox nonEst
## Horned Lark - Lizards and Snakes nonEst
## Horned Lark - Loggerhead Shrike nonEst
## Horned Lark - Mammal nonEst
## Horned Lark - Merriam's Kangaroo Rat nonEst
## Horned Lark - Nelson's Antelope Squirrel nonEst
## Horned Lark - Salinas Pocket Mouse nonEst
## Horned Lark - Say's Phoebe nonEst
## Horned Lark - Vesper Sparrow nonEst
## Horned Lark - Western whiptail nonEst
## Horned Lark - (White-tailed Antelope Squirrel) nonEst
## Kit Fox - Lizards and Snakes nonEst
## Kit Fox - Loggerhead Shrike 0.3365
## Kit Fox - Mammal nonEst
## Kit Fox - Merriam's Kangaroo Rat -0.6190
## Kit Fox - Nelson's Antelope Squirrel -1.4214
## Kit Fox - Salinas Pocket Mouse 0.8473
## Kit Fox - Say's Phoebe 0.5596
## Kit Fox - Vesper Sparrow nonEst
## Kit Fox - Western whiptail nonEst
## Kit Fox - (White-tailed Antelope Squirrel) 0.1542
## Lizards and Snakes - Loggerhead Shrike nonEst
## Lizards and Snakes - Mammal nonEst
## Lizards and Snakes - Merriam's Kangaroo Rat nonEst
## Lizards and Snakes - Nelson's Antelope Squirrel nonEst
## Lizards and Snakes - Salinas Pocket Mouse nonEst
## Lizards and Snakes - Say's Phoebe nonEst
## Lizards and Snakes - Vesper Sparrow nonEst
## Lizards and Snakes - Western whiptail nonEst
## Lizards and Snakes - (White-tailed Antelope Squirrel) nonEst
## Loggerhead Shrike - Mammal nonEst
## Loggerhead Shrike - Merriam's Kangaroo Rat -0.9555
## Loggerhead Shrike - Nelson's Antelope Squirrel -1.7579
## Loggerhead Shrike - Salinas Pocket Mouse 0.5108
## Loggerhead Shrike - Say's Phoebe 0.2231
## Loggerhead Shrike - Vesper Sparrow nonEst
## Loggerhead Shrike - Western whiptail nonEst
## Loggerhead Shrike - (White-tailed Antelope Squirrel) -0.1823
## Mammal - Merriam's Kangaroo Rat nonEst
## Mammal - Nelson's Antelope Squirrel nonEst
## Mammal - Salinas Pocket Mouse nonEst
## Mammal - Say's Phoebe nonEst
## Mammal - Vesper Sparrow nonEst
## Mammal - Western whiptail nonEst
## Mammal - (White-tailed Antelope Squirrel) nonEst
## Merriam's Kangaroo Rat - Nelson's Antelope Squirrel -0.8023
## Merriam's Kangaroo Rat - Salinas Pocket Mouse 1.4663
## Merriam's Kangaroo Rat - Say's Phoebe 1.1787
## Merriam's Kangaroo Rat - Vesper Sparrow nonEst
## Merriam's Kangaroo Rat - Western whiptail nonEst
## Merriam's Kangaroo Rat - (White-tailed Antelope Squirrel) 0.7732
## Nelson's Antelope Squirrel - Salinas Pocket Mouse 2.2687
## Nelson's Antelope Squirrel - Say's Phoebe 1.9810
## Nelson's Antelope Squirrel - Vesper Sparrow nonEst
## Nelson's Antelope Squirrel - Western whiptail nonEst
## Nelson's Antelope Squirrel - (White-tailed Antelope Squirrel) 1.5755
## Salinas Pocket Mouse - Say's Phoebe -0.2877
## Salinas Pocket Mouse - Vesper Sparrow nonEst
## Salinas Pocket Mouse - Western whiptail nonEst
## Salinas Pocket Mouse - (White-tailed Antelope Squirrel) -0.6931
## Say's Phoebe - Vesper Sparrow nonEst
## Say's Phoebe - Western whiptail nonEst
## Say's Phoebe - (White-tailed Antelope Squirrel) -0.4055
## Vesper Sparrow - Western whiptail nonEst
## Vesper Sparrow - (White-tailed Antelope Squirrel) nonEst
## Western whiptail - (White-tailed Antelope Squirrel) nonEst
## SE df z.ratio p.value
## NA NA NA NA
## NA NA NA NA
## 0.3224 Inf 10.008 <.0001
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.1500 Inf 10.273 <.0001
## 0.1228 Inf 8.385 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.2440 Inf 10.817 <.0001
## 0.0783 Inf -7.801 <.0001
## NA NA NA NA
## 0.2746 Inf 10.526 <.0001
## NA NA NA NA
## 0.3224 Inf 10.008 <.0001
## NA NA NA NA
## 0.2060 Inf 11.027 <.0001
## 0.1456 Inf 10.087 <.0001
## 0.4131 Inf 9.048 <.0001
## 0.3591 Inf 9.607 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.2955 Inf 10.304 <.0001
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.3443 Inf -4.899 0.0003
## 0.3333 Inf -6.592 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.3944 Inf -1.490 0.9991
## 0.3196 Inf -12.006 <.0001
## NA NA NA NA
## 0.4140 Inf -0.813 1.0000
## NA NA NA NA
## 0.4472 Inf 0.000 1.0000
## NA NA NA NA
## 0.3721 Inf -2.568 0.6434
## 0.3424 Inf -5.134 0.0001
## 0.5164 Inf 0.989 1.0000
## 0.4743 Inf 0.470 1.0000
## NA NA NA NA
## NA NA NA NA
## 0.4282 Inf -0.426 1.0000
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.1721 Inf -2.968 0.3313
## NA NA NA NA
## NA NA NA NA
## 0.2722 Inf 4.037 0.0127
## 0.1438 Inf -14.959 <.0001
## NA NA NA NA
## 0.2999 Inf 4.501 0.0018
## NA NA NA NA
## 0.3443 Inf 4.899 0.0003
## NA NA NA NA
## 0.2387 Inf 3.062 0.2698
## 0.1891 Inf -0.378 1.0000
## 0.4303 Inf 5.106 0.0001
## 0.3788 Inf 5.041 0.0001
## NA NA NA NA
## NA NA NA NA
## 0.3191 Inf 4.713 0.0007
## NA NA NA NA
## NA NA NA NA
## 0.2582 Inf 6.233 <.0001
## 0.1152 Inf -14.239 <.0001
## NA NA NA NA
## 0.2873 Inf 6.477 <.0001
## NA NA NA NA
## 0.3333 Inf 6.592 <.0001
## NA NA NA NA
## 0.2226 Inf 5.577 <.0001
## 0.1684 Inf 2.609 0.6102
## 0.4216 Inf 6.423 <.0001
## 0.3689 Inf 6.560 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.3073 Inf 6.556 <.0001
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.2402 Inf -13.527 <.0001
## NA NA NA NA
## 0.3563 Inf 0.705 1.0000
## NA NA NA NA
## 0.3944 Inf 1.490 0.9991
## NA NA NA NA
## 0.3066 Inf -1.199 1.0000
## 0.2698 Inf -4.337 0.0037
## 0.4714 Inf 2.331 0.8137
## 0.4249 Inf 1.908 0.9731
## NA NA NA NA
## NA NA NA NA
## 0.3727 Inf 1.088 1.0000
## NA NA NA NA
## 0.2713 Inf 12.906 <.0001
## NA NA NA NA
## 0.3196 Inf 12.006 <.0001
## NA NA NA NA
## 0.2015 Inf 14.299 <.0001
## 0.1393 Inf 14.931 <.0001
## 0.4109 Inf 10.582 <.0001
## 0.3566 Inf 11.387 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.2924 Inf 12.501 <.0001
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.4140 Inf 0.813 1.0000
## NA NA NA NA
## 0.3315 Inf -1.867 0.9792
## 0.2978 Inf -4.773 0.0005
## 0.4879 Inf 1.736 0.9917
## 0.4432 Inf 1.263 0.9999
## NA NA NA NA
## NA NA NA NA
## 0.3934 Inf 0.392 1.0000
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.3721 Inf -2.568 0.6434
## 0.3424 Inf -5.134 0.0001
## 0.5164 Inf 0.989 1.0000
## 0.4743 Inf 0.470 1.0000
## NA NA NA NA
## NA NA NA NA
## 0.4282 Inf -0.426 1.0000
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.2360 Inf -3.400 0.1122
## 0.4529 Inf 3.238 0.1755
## 0.4043 Inf 2.915 0.3684
## NA NA NA NA
## NA NA NA NA
## 0.3490 Inf 2.216 0.8770
## 0.4288 Inf 5.290 <.0001
## 0.3771 Inf 5.253 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.3171 Inf 4.968 0.0002
## 0.5401 Inf -0.533 1.0000
## NA NA NA NA
## NA NA NA NA
## 0.5000 Inf -1.386 0.9997
## NA NA NA NA
## NA NA NA NA
## 0.4564 Inf -0.888 1.0000
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
##
## Results are averaged over the levels of: microsite
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 25 estimatesanimals_density_2023 <- photo_2023 %>% group_by(site_code,microsite,plot, shrub_density, common_name) %>% summarise(captures = sum(animal.hit))## `summarise()` has grouped output by 'site_code', 'microsite', 'plot',
## 'shrub_density'. You can override using the `.groups` argument.animals_density_2023 <- animals_density_2023 %>% filter(common_name != "Blank")pca_data_2023 <- animals_density_2023 ### Created new df for pcoa data
pca_data_2023 <- pca_data_2023 %>%
spread(common_name, captures) %>%
ungroup() %>%
dplyr::select(-site_code, -microsite, -plot) %>%
replace(is.na(.),0)
dim(pca_data_2023)## [1] 23 26env_2023 <- read.csv("environment_2023.csv") ### Drop Tecopa open 1, Tecopa open 4, since they have no animal observations.
dim(env)## [1] 22 5model01 <- adonis(pca_data_2023 ~ microsite*shrub_density, data = env_2023)## 'adonis' will be deprecated: use 'adonis2' insteadmodel01## $aov.tab
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## microsite 1 0.4511 0.45114 1.6082 0.06930 0.150
## shrub_density 1 0.4482 0.44823 1.5978 0.06885 0.151
## Residuals 20 5.6105 0.28052 0.86184
## Total 22 6.5099 1.00000
##
## $call
## adonis(formula = pca_data_2023 ~ microsite * shrub_density, data = env_2023)
##
## $coefficients
## shrub_density Black-tailed Jackrabbit
## (Intercept) -10.398601 2.8438228
## microsite1 -14.216783 -3.7925408
## shrub_density 2.876923 0.4769231
## microsite1:shrub_density NA NA
## Black-throated Sparrow Blunt-nosed Leopard Lizard
## (Intercept) 4.545455e-02 0.12820513
## microsite1 -4.545455e-02 0.12820513
## shrub_density -6.331379e-18 -0.01538462
## microsite1:shrub_density NA NA
## Brewer's Blackbird California Ground Squirrel
## (Intercept) -2.5571096 -0.21794872
## microsite1 -2.6480186 -0.21794872
## shrub_density 0.4923077 0.04615385
## microsite1:shrub_density NA NA
## California Quail California Thrasher Common Raven
## (Intercept) -1.8205128 0.47435897 2.6340326
## microsite1 -1.8205128 0.47435897 1.7249417
## shrub_density 0.3384615 -0.07692308 -0.2615385
## microsite1:shrub_density NA NA NA
## Coyote Desert Cottontail Desert Iguana
## (Intercept) 3.060606 -2.3391608 4.545455e-02
## microsite1 1.606061 -2.4300699 -4.545455e-02
## shrub_density -0.200000 0.4461538 8.356568e-18
## microsite1:shrub_density NA NA NA
## Giant Kangaroo Rat Heermann's Kangaroo Rat Horned Lark
## (Intercept) 0.03263403 -7.849650 0.38461538
## microsite1 -0.05827506 -15.304196 0.38461538
## shrub_density 0.06153846 3.169231 -0.04615385
## microsite1:shrub_density NA NA NA
## Kit Fox Lizards and Snakes Loggerhead Shrike
## (Intercept) 2.1165501 2.727273e-01 -0.4801865
## microsite1 1.9347319 -2.727273e-01 -0.5710956
## shrub_density -0.3230769 -1.513076e-17 0.1230769
## microsite1:shrub_density NA NA NA
## Mammal Merriam's Kangaroo Rat
## (Intercept) -0.21794872 1.10256410
## microsite1 -0.21794872 0.10256410
## shrub_density 0.04615385 -0.09230769
## microsite1:shrub_density NA NA
## Nelson's Antelope Squirrel Salinas Pocket Mouse
## (Intercept) 0.3694639 -0.39044289
## microsite1 -0.2668998 -0.48135198
## shrub_density 0.1538462 0.09230769
## microsite1:shrub_density NA NA
## Say's Phoebe Vesper Sparrow Western whiptail
## (Intercept) 0.264568765 1.5384615 0.12820513
## microsite1 -0.008158508 1.5384615 0.12820513
## shrub_density -0.015384615 -0.1846154 -0.01538462
## microsite1:shrub_density NA NA NA
## White-tailed Antelope Squirrel
## (Intercept) 0.35547786
## microsite1 -0.09906760
## shrub_density -0.01538462
## microsite1:shrub_density NA
##
## $coef.sites
## 1 2 3 4
## (Intercept) 1.08713373 1.19242360 1.04021622 0.53368864
## microsite1 0.27702067 0.36812565 0.28650205 -0.08921598
## shrub_density -0.05750503 -0.07418322 -0.03323392 0.02592824
## microsite1:shrub_density NA NA NA NA
## 5 6 7 8
## (Intercept) 1.09169046 0.725232927 0.9149049 0.73036046
## microsite1 0.43517627 -0.003397368 0.1285966 -0.03557956
## shrub_density -0.08696283 -0.012927632 -0.0247195 0.01666357
## microsite1:shrub_density NA NA NA NA
## 9 10 11 12
## (Intercept) 1.3003752 1.4998669 0.80294716 0.64129703
## microsite1 0.6190745 0.7936762 0.05159731 -0.03324735
## shrub_density -0.1227435 -0.1491194 -0.01154984 0.01898538
## microsite1:shrub_density NA NA NA NA
## 13 14 15 16
## (Intercept) 0.99239018 0.66809163 0.63858697 -0.08681298
## microsite1 0.34039631 -0.04876636 -0.21003546 -0.87304773
## shrub_density -0.06950611 -0.01035118 0.04254088 0.14042524
## microsite1:shrub_density NA NA NA NA
## 17 18 19 20
## (Intercept) 0.17811261 0.1694050 -0.01538676 0.91285079
## microsite1 -0.64882951 -0.6891926 -0.81989153 0.22319732
## shrub_density 0.09926822 0.1072032 0.13358216 -0.04137692
## microsite1:shrub_density NA NA NA NA
## 21 22 23
## (Intercept) 0.95345668 1.13091299 0.9134932
## microsite1 0.30456994 0.48161788 0.2756840
## shrub_density -0.05633216 -0.08893335 -0.0522899
## microsite1:shrub_density NA NA NA
##
## $f.perms
## [,1] [,2]
## [1,] 0.46530682 1.02723751
## [2,] 0.88426933 0.78604776
## [3,] 0.70620154 0.63080175
## [4,] 0.78452045 0.95628203
## [5,] 0.65625235 1.82310508
## [6,] 0.41204149 0.54038847
## [7,] 0.89333851 1.00396992
## [8,] 0.41256690 1.48955056
## [9,] 0.48282021 1.02010885
## [10,] 1.14187511 2.00075243
## [11,] 0.36229674 1.56766319
## [12,] 0.58029315 0.42205591
## [13,] 0.58982293 1.60395896
## [14,] 0.40710734 0.77769454
## [15,] 0.95333565 1.88883713
## [16,] 1.89469529 1.59088448
## [17,] 2.45304109 0.63421638
## [18,] 0.69630316 1.34923615
## [19,] 0.80457920 0.77456754
## [20,] 0.15063732 0.69008846
## [21,] 1.13696558 0.42212376
## [22,] 0.57269079 0.84454886
## [23,] 1.18934103 1.11533190
## [24,] 0.83650477 0.90631662
## [25,] 0.57862928 1.42057363
## [26,] 0.61307193 0.62928553
## [27,] 0.50921759 1.41689070
## [28,] 0.60546656 0.64746795
## [29,] 1.25270446 0.48835053
## [30,] 1.26307206 2.38667203
## [31,] 0.23235340 1.61597370
## [32,] 0.25519945 0.33154146
## [33,] 0.75109231 0.42832222
## [34,] 1.06972304 0.71389876
## [35,] 1.07312335 1.24024031
## [36,] 0.83765182 1.14596365
## [37,] 0.23608720 1.38144786
## [38,] 0.23646657 0.48009064
## [39,] 0.58964491 0.37062061
## [40,] 0.48456003 0.90619211
## [41,] 0.65961997 1.24739487
## [42,] 1.25104421 1.45741741
## [43,] 0.56564461 0.54844614
## [44,] 0.25032223 0.77210011
## [45,] 0.95109709 1.22318596
## [46,] 1.46420310 1.11263496
## [47,] 0.51714262 0.62612509
## [48,] 2.76190601 0.70446752
## [49,] 1.18936147 0.16901212
## [50,] 1.64382681 0.48552713
## [51,] 3.23377952 1.82092259
## [52,] 0.24945196 1.69121480
## [53,] 1.27956261 0.95120022
## [54,] 0.96948035 1.08715516
## [55,] 1.59823567 1.08603772
## [56,] 1.04950411 0.91346544
## [57,] 1.01897396 0.45499685
## [58,] 1.15766496 0.78100845
## [59,] 1.01565455 0.69805486
## [60,] 2.31711393 0.72780980
## [61,] 0.44804372 1.71442977
## [62,] 0.79397276 0.69967097
## [63,] 1.39942111 1.26609972
## [64,] 1.68970019 1.23760441
## [65,] 0.91464806 1.36589037
## [66,] 2.53903862 1.60905130
## [67,] 0.61575439 0.49280490
## [68,] 0.41482495 2.18819519
## [69,] 0.83117127 0.46837486
## [70,] 0.76577886 2.13667526
## [71,] 0.51625655 0.48058234
## [72,] 1.46860717 1.05988007
## [73,] 0.30255506 0.40426702
## [74,] 1.29760873 1.56555964
## [75,] 1.41998330 3.24075853
## [76,] 1.24260780 0.92310513
## [77,] 1.80652569 2.14665450
## [78,] 1.91744283 0.85202856
## [79,] 1.43946643 0.77570236
## [80,] 0.77806136 0.51128660
## [81,] 0.71479324 0.73011698
## [82,] 0.53680056 0.85101026
## [83,] 1.07005151 1.11684791
## [84,] 1.71588906 1.34747867
## [85,] 0.76941961 1.16405303
## [86,] 0.63630776 0.91394931
## [87,] 0.91248219 0.98393008
## [88,] 1.00228284 0.67300761
## [89,] 0.65131253 1.75229157
## [90,] 0.77968516 1.52923439
## [91,] 1.71683735 1.69846664
## [92,] 0.61588691 1.52385656
## [93,] 1.05041722 1.15447018
## [94,] 0.93976045 0.70670096
## [95,] 0.64232490 0.82854646
## [96,] 0.70109004 0.53484380
## [97,] 1.04227843 1.28127132
## [98,] 0.62201413 1.04581002
## [99,] 0.36807410 0.87355216
## [100,] 1.24064707 1.27973479
## [101,] 0.36739973 2.13175783
## [102,] 0.48306130 0.95525639
## [103,] 0.81294670 1.34078557
## [104,] 0.95304735 1.73826827
## [105,] 0.86857800 1.21959915
## [106,] 0.40632261 0.69965638
## [107,] 0.86728796 0.58104252
## [108,] 1.41292956 1.05578068
## [109,] 0.53976593 0.41886274
## [110,] 0.91660335 1.50396749
## [111,] 1.89117847 0.99368072
## [112,] 0.81332130 1.02296833
## [113,] 1.47576577 0.66995709
## [114,] 0.77460486 0.91077866
## [115,] 0.99770113 0.56513033
## [116,] 0.98447887 0.88613353
## [117,] 0.51129048 0.91977763
## [118,] 0.98669305 2.48616172
## [119,] 1.62157610 1.26525289
## [120,] 0.93181053 1.43058869
## [121,] 1.18864718 0.68280866
## [122,] 0.53111471 2.19039419
## [123,] 1.94351497 1.11313360
## [124,] 0.13414627 1.85826877
## [125,] 1.33452938 0.86654021
## [126,] 1.65249616 0.37831285
## [127,] 0.83755390 1.37761772
## [128,] 1.50556135 1.10949992
## [129,] 0.88938730 1.62272497
## [130,] 0.56183307 1.70675602
## [131,] 0.73083010 1.31921036
## [132,] 1.17423834 2.44633501
## [133,] 0.49782917 0.45386517
## [134,] 0.47657091 1.28589391
## [135,] 0.75200720 1.43174276
## [136,] 1.00913713 3.24713773
## [137,] 1.09243827 0.81319995
## [138,] 1.40825521 1.18850727
## [139,] 0.69582573 1.00753389
## [140,] 0.93527522 2.06205516
## [141,] 0.60930843 0.38837708
## [142,] 1.26095197 1.63794420
## [143,] 1.20242140 1.38930442
## [144,] 0.46777829 1.30466432
## [145,] 1.79928052 1.36143803
## [146,] 0.52201200 1.26196633
## [147,] 0.92161132 1.15659157
## [148,] 1.11684031 0.39135684
## [149,] 0.72622248 1.36395308
## [150,] 0.51163800 1.17418613
## [151,] 2.02050677 1.93491881
## [152,] 2.75265758 1.69491729
## [153,] 1.16829912 1.73368808
## [154,] 0.70251715 0.59188409
## [155,] 0.50824134 0.68701208
## [156,] 1.83428348 0.44537161
## [157,] 1.55958705 1.48104197
## [158,] 0.87137331 0.44441998
## [159,] 0.98540066 1.04006703
## [160,] 0.64364022 0.97581283
## [161,] 0.32909581 1.39167411
## [162,] 2.05990174 2.12633028
## [163,] 1.07255914 1.80083008
## [164,] 1.39788039 1.25723004
## [165,] 2.14211229 0.69573215
## [166,] 0.54414691 0.84363510
## [167,] 1.82794380 2.20311860
## [168,] 1.23042487 1.32745948
## [169,] 0.66844433 1.07058347
## [170,] 0.83984098 0.45841726
## [171,] 0.70076220 0.88839508
## [172,] 0.57886695 0.60981158
## [173,] 0.76004397 0.90969644
## [174,] 1.34257837 1.09095278
## [175,] 0.57381778 1.72115098
## [176,] 0.70737063 0.90565490
## [177,] 1.06482290 0.93910026
## [178,] 2.00828175 0.63893664
## [179,] 0.40013174 1.50895199
## [180,] 1.21813551 1.65409718
## [181,] 0.90186247 0.41548422
## [182,] 0.42251282 0.72029939
## [183,] 1.27949673 1.21985913
## [184,] 1.52206229 1.05129101
## [185,] 0.88868450 1.11253361
## [186,] 0.27949835 1.45173308
## [187,] 0.94198928 0.33061383
## [188,] 0.49606202 1.07958765
## [189,] 1.55663285 0.49993530
## [190,] 0.85455453 0.34557739
## [191,] 0.96487380 1.29793929
## [192,] 1.65445145 1.84665853
## [193,] 1.11098788 0.66271960
## [194,] 0.87041731 0.63957031
## [195,] 0.66760746 1.47997546
## [196,] 0.66560114 0.53447970
## [197,] 0.71200113 1.02318158
## [198,] 0.70367066 0.33632519
## [199,] 2.71660719 1.81886444
## [200,] 0.74691285 0.49110802
## [201,] 0.30103464 0.70357336
## [202,] 0.78327949 1.55348509
## [203,] 1.37772334 0.87399868
## [204,] 1.95543865 0.68288126
## [205,] 0.77387786 1.17054965
## [206,] 1.75522668 0.97407892
## [207,] 1.28849346 0.79074527
## [208,] 1.04858328 1.14006317
## [209,] 1.38259599 0.41257835
## [210,] 1.25800193 0.96097196
## [211,] 0.53722954 1.13412658
## [212,] 0.76084481 0.96122596
## [213,] 1.08406523 1.06944877
## [214,] 0.66478212 0.92955945
## [215,] 1.14399399 1.00121488
## [216,] 0.67169760 0.67117778
## [217,] 0.58884603 0.57924801
## [218,] 1.84274594 1.62425236
## [219,] 1.42065302 0.51100036
## [220,] 0.54931118 1.79362298
## [221,] 0.49343252 1.01033924
## [222,] 1.16382618 0.82247626
## [223,] 0.58572000 0.80228356
## [224,] 0.77194482 1.18508892
## [225,] 3.03843729 1.19597257
## [226,] 1.28824066 0.85717100
## [227,] 1.93086773 2.07343619
## [228,] 1.54333170 1.80324181
## [229,] 0.46177968 0.88294422
## [230,] 0.57727315 0.56198887
## [231,] 0.85779276 1.20881861
## [232,] 1.74246235 0.77493753
## [233,] 0.89064864 1.07117332
## [234,] 3.39855196 0.64585340
## [235,] 2.19430429 0.69914624
## [236,] 0.99540729 0.39336771
## [237,] 2.58908873 0.44138581
## [238,] 0.35905719 0.31655864
## [239,] 0.66168071 0.99287647
## [240,] 2.12578691 0.50881799
## [241,] 0.57358727 1.08865141
## [242,] 0.60157872 2.75099387
## [243,] 0.93961432 0.38170589
## [244,] 1.53163044 0.90700258
## [245,] 0.85721071 1.08690777
## [246,] 1.53078070 0.66524922
## [247,] 1.00367672 0.87379937
## [248,] 1.03671711 1.35062214
## [249,] 0.95078428 0.72492618
## [250,] 0.77757221 1.20733014
## [251,] 0.69646531 0.98328585
## [252,] 0.54167935 0.98817772
## [253,] 0.91450682 0.35274037
## [254,] 0.28636514 1.90832255
## [255,] 0.41354135 1.49257191
## [256,] 1.24254963 0.60553741
## [257,] 0.97577600 2.22481644
## [258,] 0.77708379 1.30804286
## [259,] 0.80046292 0.78418576
## [260,] 1.91563857 0.31955859
## [261,] 0.75130378 0.07262442
## [262,] 0.62025098 0.31996274
## [263,] 1.31011357 0.69571487
## [264,] 0.37633799 1.83843331
## [265,] 0.72110436 0.74762501
## [266,] 1.10922897 0.59321359
## [267,] 1.40052235 1.83625895
## [268,] 0.91145502 0.68624908
## [269,] 1.29684060 1.21255829
## [270,] 0.62106951 1.71606099
## [271,] 0.66972124 1.60828643
## [272,] 1.01889127 0.85638632
## [273,] 0.42175103 1.19553012
## [274,] 0.67058203 1.01442465
## [275,] 2.52533082 0.84107598
## [276,] 1.06385864 0.90993279
## [277,] 1.51749637 1.59968364
## [278,] 0.64845669 0.54590064
## [279,] 1.10208444 0.71323201
## [280,] 0.46322610 0.30627824
## [281,] 0.57236329 1.49264254
## [282,] 1.37909826 0.07157821
## [283,] 0.51797181 1.39246476
## [284,] 1.07796604 0.58171822
## [285,] 1.78415828 1.03930263
## [286,] 0.50706414 0.82902199
## [287,] 1.19960424 0.56430012
## [288,] 2.13528173 1.54812720
## [289,] 0.80455349 1.20175721
## [290,] 0.91592213 0.97958968
## [291,] 0.60963299 1.38386943
## [292,] 1.37232870 1.64875112
## [293,] 0.48970779 1.18371934
## [294,] 2.13407573 0.62765483
## [295,] 1.64479381 2.37694090
## [296,] 1.19969301 1.24729495
## [297,] 2.17518176 0.88529303
## [298,] 1.42980211 1.64953895
## [299,] 0.80011632 0.95622167
## [300,] 0.72438753 0.64805834
## [301,] 1.44836822 0.49478082
## [302,] 2.38547500 1.02163196
## [303,] 0.65222793 0.46724732
## [304,] 1.03647239 1.87407226
## [305,] 1.82202668 1.52545811
## [306,] 0.79010247 0.63241906
## [307,] 0.69700611 1.42728965
## [308,] 0.70199248 0.78271593
## [309,] 0.40426735 0.51812330
## [310,] 0.77126264 0.33042731
## [311,] 2.45913810 1.31303611
## [312,] 0.46745239 1.20446379
## [313,] 0.55607217 0.71823225
## [314,] 0.67332900 0.54343125
## [315,] 1.38229328 1.29640146
## [316,] 1.42645097 0.26366943
## [317,] 0.60510221 0.62097231
## [318,] 0.91563832 1.00015286
## [319,] 1.21435845 0.42227639
## [320,] 1.32152486 0.77688942
## [321,] 1.45640537 1.64735934
## [322,] 0.50841608 0.16139729
## [323,] 0.56043721 0.24493033
## [324,] 1.37889741 1.45756898
## [325,] 0.91384127 0.98391988
## [326,] 0.81392436 2.23013229
## [327,] 0.82807832 0.56237698
## [328,] 1.10166081 0.96834243
## [329,] 1.58292481 1.49520460
## [330,] 0.37662122 1.52129685
## [331,] 0.89300582 1.19523649
## [332,] 0.84560598 0.29121073
## [333,] 2.25785004 1.01681017
## [334,] 0.47269119 1.42113289
## [335,] 0.88196630 1.19019127
## [336,] 1.49632386 1.92747739
## [337,] 2.77998162 1.42693252
## [338,] 0.28840349 0.52713450
## [339,] 0.92067133 1.73577314
## [340,] 1.00804286 1.26603823
## [341,] 1.11246170 1.69728670
## [342,] 0.91133494 1.16587038
## [343,] 1.76445589 0.53811682
## [344,] 1.27925321 0.46152030
## [345,] 0.52825138 0.60843543
## [346,] 1.70997835 1.96168680
## [347,] 1.03923684 1.14776435
## [348,] 0.40502762 0.89708071
## [349,] 1.28350973 1.23013715
## [350,] 1.25564540 0.42229789
## [351,] 1.64258227 1.09171404
## [352,] 0.92118758 0.56748410
## [353,] 0.86040032 0.81863130
## [354,] 1.69899572 0.85490511
## [355,] 1.22596355 1.79188674
## [356,] 0.71469190 1.45335000
## [357,] 2.80540830 1.39858704
## [358,] 2.86840302 0.81154718
## [359,] 0.28171695 0.84701828
## [360,] 0.92875620 0.96019518
## [361,] 0.77666180 1.43979798
## [362,] 1.77694007 0.83408601
## [363,] 1.23991085 0.91210485
## [364,] 0.76604896 1.32249074
## [365,] 0.60024124 0.12059228
## [366,] 1.08187259 0.73623924
## [367,] 2.92321521 0.29742093
## [368,] 0.72660121 1.19351945
## [369,] 0.34425760 1.83367118
## [370,] 0.35538003 2.13729145
## [371,] 0.83995701 1.00523904
## [372,] 1.55806804 0.53720524
## [373,] 0.23208583 1.69158297
## [374,] 1.32888888 0.62780927
## [375,] 1.33015852 0.25612282
## [376,] 2.01560849 1.09767665
## [377,] 0.70254963 1.48837277
## [378,] 2.07068218 0.25140776
## [379,] 2.23828743 1.25234220
## [380,] 1.52702640 1.26963750
## [381,] 1.04599784 1.55395520
## [382,] 0.98173056 0.52918565
## [383,] 1.12644235 0.77091087
## [384,] 0.47249533 1.02493022
## [385,] 0.70300630 1.37855371
## [386,] 0.45133351 0.65320519
## [387,] 0.87310279 1.05575329
## [388,] 1.71396186 2.20005523
## [389,] 1.03085621 0.30756693
## [390,] 0.82115052 1.32512309
## [391,] 0.51298535 1.11216001
## [392,] 1.22773049 1.02291168
## [393,] 0.71928070 0.79206802
## [394,] 0.88513582 0.48610556
## [395,] 0.60650896 1.92161069
## [396,] 0.32687116 1.34095685
## [397,] 0.98494295 0.79868640
## [398,] 0.31600148 0.84668829
## [399,] 1.54294007 2.58940897
## [400,] 0.89588448 1.01550098
## [401,] 2.28994111 0.65747664
## [402,] 4.41191444 0.22401333
## [403,] 2.73143490 0.72450080
## [404,] 0.87697012 1.25030646
## [405,] 0.45216677 0.76694316
## [406,] 0.70067514 0.80051945
## [407,] 0.75979745 0.23896808
## [408,] 0.27000233 0.75627706
## [409,] 0.95617993 1.12102579
## [410,] 0.62499418 0.37919900
## [411,] 1.19684170 1.64327055
## [412,] 2.78319061 0.42623559
## [413,] 0.56950238 0.44949989
## [414,] 1.74633602 1.00657830
## [415,] 2.35666491 1.22441415
## [416,] 0.16590799 0.30864236
## [417,] 1.16484818 1.43991777
## [418,] 0.14696948 0.70919240
## [419,] 0.63264480 1.65807751
## [420,] 1.77994860 1.99301919
## [421,] 0.86365838 2.69104384
## [422,] 0.28507649 1.28688735
## [423,] 0.18101255 1.64793665
## [424,] 1.63531146 1.66592224
## [425,] 0.40162653 0.50776011
## [426,] 0.82068211 1.23753455
## [427,] 0.70313432 3.02318829
## [428,] 0.47086593 1.14565366
## [429,] 0.78714267 0.36614885
## [430,] 0.62561546 1.36641342
## [431,] 1.90555025 0.89136650
## [432,] 0.67337634 0.13314496
## [433,] 0.56207331 0.25761564
## [434,] 0.72778845 0.98278122
## [435,] 0.88895133 0.69566003
## [436,] 0.93942277 1.13326425
## [437,] 0.84383700 0.45213874
## [438,] 0.66707673 1.05790886
## [439,] 1.90780761 0.59883314
## [440,] 0.54188714 1.31729211
## [441,] 0.91323614 0.30014274
## [442,] 0.37357132 1.46881595
## [443,] 1.03320292 1.09180192
## [444,] 0.37417062 0.19169805
## [445,] 0.76510285 0.59544493
## [446,] 1.45121809 0.91073683
## [447,] 1.09856577 0.79492290
## [448,] 1.18696285 2.61183520
## [449,] 0.71628968 1.11678650
## [450,] 2.41690443 1.53018450
## [451,] 0.50376716 0.54596686
## [452,] 1.23721379 0.35256826
## [453,] 1.11616603 0.42227063
## [454,] 0.73835076 0.66583788
## [455,] 0.99972562 0.70327182
## [456,] 1.47044175 1.99405551
## [457,] 0.45858427 0.74675260
## [458,] 2.04814710 0.82251696
## [459,] 0.90411216 0.78114508
## [460,] 1.04298560 0.45584395
## [461,] 0.60788382 0.87641161
## [462,] 1.04704938 0.69966783
## [463,] 0.45008053 0.98270338
## [464,] 0.55624734 0.53650210
## [465,] 1.12663784 2.23036221
## [466,] 0.25767619 0.76784483
## [467,] 0.11154732 0.80741817
## [468,] 1.05579222 0.79517963
## [469,] 0.30576819 1.46541962
## [470,] 0.58643384 0.57697974
## [471,] 0.63282140 0.88275589
## [472,] 1.69641191 0.88984517
## [473,] 1.26717993 2.15163482
## [474,] 1.10516341 0.37598780
## [475,] 1.12399190 0.44119726
## [476,] 1.26130020 1.22436258
## [477,] 0.56804638 0.77268792
## [478,] 1.80909834 1.41945298
## [479,] 0.82277439 1.65438904
## [480,] 0.50178543 1.54936044
## [481,] 0.81502051 1.01937551
## [482,] 1.42383757 2.29273047
## [483,] 0.49734544 0.55751165
## [484,] 0.47659967 1.86007195
## [485,] 0.73075056 0.60484229
## [486,] 0.17494451 1.27270791
## [487,] 1.74769338 1.57171402
## [488,] 1.28571703 1.02132470
## [489,] 0.65603780 0.55651703
## [490,] 1.26904784 1.05863526
## [491,] 1.80205559 1.16863148
## [492,] 0.76203519 0.56957529
## [493,] 1.71534101 2.46182630
## [494,] 0.81541606 1.20958633
## [495,] 1.68260093 0.91081364
## [496,] 0.96018824 0.80250587
## [497,] 0.87491248 0.88146856
## [498,] 0.86699009 0.84679673
## [499,] 1.16663417 0.98938932
## [500,] 1.50670022 0.79339340
## [501,] 1.08882393 0.20229786
## [502,] 1.34060678 1.32269829
## [503,] 1.64373385 1.23044231
## [504,] 1.13339030 1.32501154
## [505,] 0.44164934 0.44883522
## [506,] 0.65104301 0.36332243
## [507,] 1.65350427 1.47538026
## [508,] 1.07421347 1.59135889
## [509,] 0.66086247 2.33672469
## [510,] 0.58632752 1.99962425
## [511,] 0.26637763 1.24418891
## [512,] 1.21853375 0.70356831
## [513,] 0.32538953 1.49292646
## [514,] 1.33692127 0.86178500
## [515,] 2.81626496 1.20190845
## [516,] 0.42683567 1.12698261
## [517,] 0.53704919 0.87752195
## [518,] 0.56979497 1.05929277
## [519,] 1.18231474 2.07642426
## [520,] 1.66419676 0.56564426
## [521,] 0.92953837 1.15432756
## [522,] 0.67020900 0.75030154
## [523,] 0.74534706 0.45097931
## [524,] 0.54135387 0.72944945
## [525,] 2.35947635 0.62157709
## [526,] 0.16511604 1.33049329
## [527,] 1.41231635 2.17425979
## [528,] 2.17124925 2.09428628
## [529,] 1.04973121 1.70561948
## [530,] 0.92325348 0.56117506
## [531,] 2.52168888 0.68363758
## [532,] 0.75941498 1.12350138
## [533,] 0.74541266 0.54046150
## [534,] 0.74350808 0.32508951
## [535,] 1.23163565 0.63380639
## [536,] 0.47128293 0.06440537
## [537,] 0.89301416 0.95596221
## [538,] 1.18978120 1.03389319
## [539,] 0.58226293 2.68670556
## [540,] 1.76510447 0.69766858
## [541,] 1.46501459 0.69640242
## [542,] 0.45229471 1.13887401
## [543,] 3.18681960 1.39411796
## [544,] 0.35133083 1.33235866
## [545,] 1.00100489 0.64685776
## [546,] 1.32577038 1.14147262
## [547,] 0.63507532 1.80892575
## [548,] 2.66040905 0.91707794
## [549,] 1.83567893 1.13265916
## [550,] 0.98671065 1.39266161
## [551,] 1.30638771 0.78435682
## [552,] 1.53096596 0.38065218
## [553,] 0.65331450 0.99385507
## [554,] 1.75276159 1.31681781
## [555,] 0.25390550 0.98766575
## [556,] 0.48586543 1.35153464
## [557,] 1.08160229 1.30865338
## [558,] 1.28560983 0.72668801
## [559,] 0.38255865 1.61644644
## [560,] 0.62248114 0.72987688
## [561,] 1.18565836 1.06854593
## [562,] 0.63824334 1.71211965
## [563,] 1.21002241 1.56315871
## [564,] 0.61941196 0.31559441
## [565,] 1.67341991 1.01839761
## [566,] 1.12321911 1.47345408
## [567,] 0.40089470 1.23441487
## [568,] 0.63769485 2.09147858
## [569,] 1.24486533 1.12220566
## [570,] 0.78564579 1.08061702
## [571,] 2.22935584 0.28598602
## [572,] 1.90208629 2.16702425
## [573,] 1.66935618 1.71889417
## [574,] 1.19900764 1.19051768
## [575,] 0.62766805 0.78524603
## [576,] 1.28445853 0.78980169
## [577,] 1.86484644 2.34963179
## [578,] 1.03829249 1.59728215
## [579,] 0.63671384 0.75044895
## [580,] 2.50292491 0.93780208
## [581,] 0.37516371 0.59890162
## [582,] 0.48996392 1.52667970
## [583,] 0.84307879 1.18399962
## [584,] 0.75631322 0.80190642
## [585,] 1.16827988 0.73508122
## [586,] 2.27347642 1.07754478
## [587,] 0.89686664 1.83090309
## [588,] 0.74283516 0.96726170
## [589,] 1.16454632 1.33286414
## [590,] 1.24404793 1.44060689
## [591,] 1.60977098 1.78888108
## [592,] 0.99131616 0.63625552
## [593,] 0.98073238 0.66520460
## [594,] 1.01679951 1.43951787
## [595,] 1.00433221 1.19171935
## [596,] 0.49212137 0.59876398
## [597,] 0.72732049 0.30557612
## [598,] 0.71362952 0.69008147
## [599,] 1.11159525 0.34646844
## [600,] 2.00884113 1.65720907
## [601,] 0.62772671 0.91317700
## [602,] 1.30121045 1.69172382
## [603,] 0.71036415 1.26388622
## [604,] 1.27706045 0.45624611
## [605,] 2.22044770 0.59001624
## [606,] 1.51558860 1.06470252
## [607,] 0.47803283 0.40454637
## [608,] 2.19996700 2.05975168
## [609,] 1.80480165 0.05907415
## [610,] 2.08356367 1.20237091
## [611,] 1.59582933 1.68143149
## [612,] 0.47271249 0.95826757
## [613,] 0.40382286 0.55853308
## [614,] 1.01307912 2.18091574
## [615,] 0.60233015 0.79918225
## [616,] 1.09009265 0.54330031
## [617,] 1.81616384 1.51231752
## [618,] 1.47209049 1.28719389
## [619,] 0.59686268 0.24376475
## [620,] 0.71795933 0.71062170
## [621,] 0.45170542 1.00393586
## [622,] 0.51719015 1.82516019
## [623,] 1.26477670 0.80437741
## [624,] 1.59283420 1.41972140
## [625,] 0.25930215 0.55027087
## [626,] 1.23112616 0.76449845
## [627,] 0.19529858 0.33440116
## [628,] 0.91177346 2.28568696
## [629,] 0.68974129 0.71167988
## [630,] 0.78595614 0.42168081
## [631,] 0.76992509 1.26912726
## [632,] 1.29915270 0.56109083
## [633,] 0.20474651 1.03397507
## [634,] 1.27267828 0.80808972
## [635,] 1.81401352 1.09141129
## [636,] 0.82354846 1.03765844
## [637,] 0.93069278 1.17030052
## [638,] 0.54519290 0.36851374
## [639,] 0.64603993 0.77543366
## [640,] 0.88082500 0.20232891
## [641,] 0.79855622 1.01308145
## [642,] 1.14376266 2.00900053
## [643,] 0.47705972 0.48297703
## [644,] 1.45388143 1.03062923
## [645,] 0.79224936 1.22357240
## [646,] 0.62502358 1.00599605
## [647,] 2.13153701 1.60434553
## [648,] 1.66196902 0.58885346
## [649,] 0.74242778 1.68312606
## [650,] 0.90770363 0.70970127
## [651,] 2.05902837 1.00935014
## [652,] 1.31594481 0.44081037
## [653,] 1.51873454 0.40077612
## [654,] 0.56920896 1.01841410
## [655,] 0.96682206 0.90113297
## [656,] 0.61813417 0.68985043
## [657,] 1.43788196 2.30596089
## [658,] 1.38351428 0.80354737
## [659,] 0.97140525 0.89179752
## [660,] 1.09958776 0.47628500
## [661,] 1.85878410 0.68967588
## [662,] 1.78831839 0.21004039
## [663,] 0.19578523 0.67381809
## [664,] 0.73318758 0.40480387
## [665,] 2.59211359 0.24161558
## [666,] 0.59893711 1.62265649
## [667,] 0.85179708 0.92419481
## [668,] 0.24411332 0.30596091
## [669,] 1.31311636 1.52722177
## [670,] 1.10997389 0.23736345
## [671,] 1.06878124 0.97423772
## [672,] 1.09009709 1.67234844
## [673,] 0.49896324 0.64922086
## [674,] 0.81276096 1.89428970
## [675,] 1.26611323 0.42244865
## [676,] 0.98759782 1.16934923
## [677,] 0.84542633 0.55506035
## [678,] 1.59116691 0.77960486
## [679,] 0.74183314 0.66936736
## [680,] 0.73715435 1.17574445
## [681,] 0.39988470 0.71445234
## [682,] 0.40765885 0.51412389
## [683,] 0.68484366 1.19166494
## [684,] 0.40929450 0.47773048
## [685,] 1.54431991 1.12294344
## [686,] 1.29634746 0.63095202
## [687,] 0.65242003 0.64499235
## [688,] 0.84655772 0.71860441
## [689,] 0.65887210 1.28227541
## [690,] 0.59879224 0.86005096
## [691,] 0.51365916 1.37011153
## [692,] 0.31277240 1.40899868
## [693,] 1.33742112 0.46207699
## [694,] 1.79956224 1.09422541
## [695,] 0.90063815 1.14054657
## [696,] 1.92610451 2.76604802
## [697,] 1.09862636 1.15894212
## [698,] 0.80525351 1.96218430
## [699,] 0.69888540 0.73513398
## [700,] 0.74703455 0.34379657
## [701,] 0.82878274 0.50914062
## [702,] 1.34772119 0.41788635
## [703,] 2.05977059 0.37884983
## [704,] 0.93779800 1.48301280
## [705,] 1.74010724 0.68327549
## [706,] 1.32456916 0.47980830
## [707,] 0.42162042 1.32521920
## [708,] 0.79797258 0.38981945
## [709,] 0.67769598 1.31225224
## [710,] 0.28193480 1.45015399
## [711,] 0.82540986 1.10354493
## [712,] 0.52794307 1.18293619
## [713,] 1.15669221 0.92652165
## [714,] 0.91824252 2.46954645
## [715,] 1.50684802 0.55075907
## [716,] 0.29308527 0.72095605
## [717,] 1.04268318 1.07231305
## [718,] 0.58359615 1.21954555
## [719,] 1.47098059 0.49855718
## [720,] 1.48084715 0.97248057
## [721,] 0.42059568 0.48714215
## [722,] 1.30976634 1.19626947
## [723,] 1.06218257 1.46461071
## [724,] 0.50323296 1.70440336
## [725,] 2.00346118 0.96413146
## [726,] 0.53304691 1.94526192
## [727,] 0.93573104 1.54852620
## [728,] 0.44424093 0.53788224
## [729,] 0.62376174 0.76646992
## [730,] 1.40859578 0.65276482
## [731,] 1.40249692 0.52285462
## [732,] 1.15721254 1.89114430
## [733,] 1.03608608 0.45284090
## [734,] 0.58621713 1.00552310
## [735,] 1.56992725 1.19843172
## [736,] 0.87014018 0.60363841
## [737,] 0.95938960 0.78043886
## [738,] 0.86674651 0.63415102
## [739,] 2.10010337 0.34946959
## [740,] 0.89058571 0.37816180
## [741,] 0.87345482 0.91400375
## [742,] 0.33455789 0.59056832
## [743,] 1.09608866 0.55840844
## [744,] 0.44544002 1.20725731
## [745,] 1.28826760 1.61228873
## [746,] 1.01345279 1.37232449
## [747,] 1.26350376 0.67658429
## [748,] 1.25190340 1.44747547
## [749,] 1.27863555 0.58983274
## [750,] 0.71892450 0.36714381
## [751,] 0.71684762 1.53571811
## [752,] 0.68603043 1.03450557
## [753,] 0.41205998 1.93692000
## [754,] 0.50608051 1.17464774
## [755,] 0.27533791 0.78894147
## [756,] 1.12427367 2.13090474
## [757,] 1.51139079 1.59280830
## [758,] 0.33947811 0.71355231
## [759,] 0.71507160 2.59816209
## [760,] 2.09234608 0.69258651
## [761,] 2.59373532 1.24923545
## [762,] 0.97872560 0.80804254
## [763,] 1.01046653 0.63431090
## [764,] 0.89466095 1.26004414
## [765,] 1.52505664 0.93192125
## [766,] 0.65600678 1.55014148
## [767,] 0.61492283 0.77335023
## [768,] 0.75713668 0.34716449
## [769,] 1.00082548 1.31670123
## [770,] 1.40659152 1.83096463
## [771,] 0.23604628 1.57065173
## [772,] 0.39847235 0.47639535
## [773,] 0.52017362 1.18383054
## [774,] 0.49184172 1.07053130
## [775,] 2.56787104 0.74042433
## [776,] 0.43728386 1.03076455
## [777,] 0.85319250 0.55859613
## [778,] 0.49222476 1.31369747
## [779,] 0.32164914 0.15110377
## [780,] 1.28600258 0.79744085
## [781,] 0.88979710 0.42592599
## [782,] 1.68947028 3.03491492
## [783,] 1.18989534 0.30871490
## [784,] 1.92428386 0.38726332
## [785,] 0.38437407 1.20220129
## [786,] 0.54221040 1.37099317
## [787,] 0.38168445 0.76633890
## [788,] 1.03915104 1.02235257
## [789,] 1.03084637 1.64684671
## [790,] 0.43061080 0.56642102
## [791,] 0.51328456 1.34688260
## [792,] 1.29644970 2.85866263
## [793,] 0.29692717 1.34853639
## [794,] 0.47025639 0.29698707
## [795,] 1.43226220 0.83750070
## [796,] 0.89525978 0.94574904
## [797,] 1.01548792 1.34242671
## [798,] 0.53487491 0.81501099
## [799,] 1.60291748 2.87071701
## [800,] 2.40589062 0.92662275
## [801,] 0.89229824 1.19750688
## [802,] 0.52245817 1.23072522
## [803,] 0.66333178 1.62519208
## [804,] 0.86317843 1.05664454
## [805,] 0.96383731 0.96521547
## [806,] 1.03285350 0.79248390
## [807,] 1.46118471 0.78940838
## [808,] 1.08545295 0.86022175
## [809,] 0.43867199 1.04348941
## [810,] 0.34237897 0.26747360
## [811,] 1.06087144 2.36608147
## [812,] 1.22402335 0.36713060
## [813,] 1.09436705 0.34783260
## [814,] 0.87654088 0.69008613
## [815,] 0.23048964 1.64716060
## [816,] 1.32844259 0.39071572
## [817,] 1.59733762 2.42567485
## [818,] 2.31894123 2.20890400
## [819,] 1.79336059 0.93774208
## [820,] 0.38595697 0.31228763
## [821,] 0.54853247 0.73380999
## [822,] 1.95429373 1.61757122
## [823,] 0.84709130 0.36778146
## [824,] 1.34862736 1.38422571
## [825,] 1.28158941 1.48996730
## [826,] 1.42333993 1.64183321
## [827,] 0.53614003 1.17500527
## [828,] 1.89775023 0.73837374
## [829,] 1.01919020 0.69129810
## [830,] 0.52752389 2.44558029
## [831,] 0.92894097 0.66764360
## [832,] 1.15387385 1.08354661
## [833,] 1.62051849 2.27909122
## [834,] 1.10286058 0.51916630
## [835,] 0.37372946 0.44865874
## [836,] 0.80461010 0.85691576
## [837,] 0.92769680 0.77493997
## [838,] 1.25660622 0.64623661
## [839,] 1.11020383 1.20550121
## [840,] 0.41985457 0.48252745
## [841,] 0.58316789 0.37142260
## [842,] 0.55927565 3.01739196
## [843,] 2.49294857 0.53028335
## [844,] 0.59207743 1.13499678
## [845,] 0.81168199 0.50509652
## [846,] 0.63828587 2.49604946
## [847,] 0.11687059 0.56405744
## [848,] 0.92071700 1.25707882
## [849,] 0.62811910 0.54452991
## [850,] 0.68864058 2.34108360
## [851,] 1.98813449 2.15758482
## [852,] 0.96636553 1.15987528
## [853,] 0.50012678 0.61436256
## [854,] 0.58883262 0.84292190
## [855,] 1.07669410 0.53429298
## [856,] 1.35131311 0.47019266
## [857,] 1.99604596 0.70804712
## [858,] 0.50319118 0.80845133
## [859,] 1.28516871 1.18583863
## [860,] 0.70068280 1.25085833
## [861,] 1.14200310 1.41738918
## [862,] 0.66431516 0.73082911
## [863,] 0.60222834 1.30557232
## [864,] 2.15892463 0.71163008
## [865,] 0.29001653 0.21353531
## [866,] 0.67092943 0.73784575
## [867,] 0.54472953 1.20616442
## [868,] 0.92688556 1.03085096
## [869,] 0.65136718 0.82225178
## [870,] 0.48609269 0.62278110
## [871,] 1.20615815 0.49707669
## [872,] 1.23351914 1.07788033
## [873,] 1.64521706 1.99862264
## [874,] 0.57741470 0.52914935
## [875,] 0.34012342 0.47300133
## [876,] 0.45124916 0.40009707
## [877,] 0.70131351 0.50857264
## [878,] 1.37888497 1.12916714
## [879,] 2.50002322 0.69370740
## [880,] 1.02864162 1.15426854
## [881,] 1.29019573 1.00376523
## [882,] 1.05102140 1.31999938
## [883,] 0.74388326 0.76755485
## [884,] 1.25291982 0.48428857
## [885,] 1.54070068 0.97412670
## [886,] 0.73817695 0.71396321
## [887,] 1.03070231 0.80910627
## [888,] 0.70733045 0.70076468
## [889,] 1.49814598 0.53227756
## [890,] 0.53024812 0.92260908
## [891,] 1.08486925 1.93509196
## [892,] 0.59940635 0.72311177
## [893,] 0.57450092 1.35341804
## [894,] 1.79405073 1.07477944
## [895,] 0.21499918 0.35022510
## [896,] 0.56998174 0.72052021
## [897,] 1.02402833 2.41580674
## [898,] 1.52631826 1.50248164
## [899,] 1.50824926 1.07372116
## [900,] 0.49334491 1.06871818
## [901,] 1.41522717 2.84522148
## [902,] 0.82790067 2.36896053
## [903,] 1.60375665 0.79416784
## [904,] 0.31340902 2.78047995
## [905,] 0.85047621 1.11246046
## [906,] 0.87359907 1.19679814
## [907,] 0.35962695 0.72873514
## [908,] 2.27598336 1.58114496
## [909,] 0.64843460 0.31206881
## [910,] 0.42488924 0.65103151
## [911,] 0.62593719 0.91524567
## [912,] 2.22092014 0.74220303
## [913,] 0.98008490 0.92449568
## [914,] 0.63629436 0.90078628
## [915,] 0.71336855 0.63313088
## [916,] 0.83124065 1.04338417
## [917,] 1.55360325 0.69699097
## [918,] 0.88758911 1.48967691
## [919,] 0.81449812 0.72860423
## [920,] 0.39791171 0.88909461
## [921,] 0.84335710 0.56526315
## [922,] 1.70928303 1.28024332
## [923,] 0.61505221 1.02085007
## [924,] 1.57561851 1.09573411
## [925,] 0.94258983 1.05489614
## [926,] 0.63001795 0.91220001
## [927,] 1.15983174 1.00649251
## [928,] 0.79192310 1.32475186
## [929,] 0.32612830 0.53902589
## [930,] 0.06507824 0.46683767
## [931,] 0.99942698 1.01099141
## [932,] 0.85819718 1.35158553
## [933,] 2.27027143 0.70256989
## [934,] 0.85748615 1.08375184
## [935,] 0.50984221 0.33737360
## [936,] 1.88506174 0.20621667
## [937,] 0.97008207 1.01653288
## [938,] 0.66035613 0.70767619
## [939,] 0.60292447 0.56466460
## [940,] 1.65659554 1.75001230
## [941,] 1.14036195 1.38914840
## [942,] 0.68841710 1.46491361
## [943,] 1.99922885 0.41672160
## [944,] 0.38753706 1.13124173
## [945,] 1.24490350 1.28126704
## [946,] 0.72778573 1.44571945
## [947,] 0.54906314 0.58453882
## [948,] 1.81598942 0.43549249
## [949,] 0.34033894 0.79579911
## [950,] 0.80848042 0.89848907
## [951,] 1.03917435 0.62845041
## [952,] 1.34181578 1.46777137
## [953,] 0.69368863 0.81119101
## [954,] 1.17242356 0.52461616
## [955,] 1.86842717 1.59466127
## [956,] 0.66835349 1.04369243
## [957,] 0.44460128 1.48373771
## [958,] 1.27759082 0.72572174
## [959,] 1.32060817 0.60752432
## [960,] 0.80202177 0.90071903
## [961,] 0.60817740 0.45518140
## [962,] 1.61741602 0.16625947
## [963,] 0.17790034 1.63388810
## [964,] 1.10735725 1.47725023
## [965,] 1.10967454 2.39772392
## [966,] 0.51993983 1.28254404
## [967,] 0.36946811 1.26305619
## [968,] 0.71352134 1.37349992
## [969,] 1.04524829 1.38124881
## [970,] 0.40747450 0.40406030
## [971,] 0.81115614 1.53944929
## [972,] 1.10944411 0.85249147
## [973,] 1.01315505 0.46267533
## [974,] 1.97265014 0.71529619
## [975,] 0.69378163 1.35512595
## [976,] 1.26242201 0.38993369
## [977,] 0.40984163 0.79500240
## [978,] 1.01136820 0.90020576
## [979,] 1.25290904 0.27863702
## [980,] 1.89168281 1.07017774
## [981,] 0.52213994 1.78279136
## [982,] 0.50858630 1.15337107
## [983,] 0.52640670 0.86512148
## [984,] 0.57360037 1.16488051
## [985,] 1.29013824 0.70920965
## [986,] 0.73614325 1.25243904
## [987,] 0.98448407 0.42224632
## [988,] 1.47705088 0.70825188
## [989,] 0.22408898 0.28103410
## [990,] 2.19876000 0.39834852
## [991,] 1.78030767 1.17992941
## [992,] 1.34823547 1.47540577
## [993,] 0.86095407 1.18363061
## [994,] 0.84773532 0.16795104
## [995,] 0.60582437 1.31650125
## [996,] 0.75056417 1.51325566
## [997,] 0.85279635 0.33303172
## [998,] 1.00470509 1.63167659
## [999,] 1.02546663 0.91057727
##
## $model.matrix
## (Intercept) microsite1 shrub_density
## 1 1 1 11
## 2 1 1 12
## 3 1 -1 0
## 4 1 -1 0
## 5 1 1 11
## 6 1 1 10
## 7 1 -1 0
## 8 1 -1 0
## 9 1 1 14
## 10 1 1 13
## 11 1 -1 0
## 12 1 -1 0
## 13 1 1 11
## 14 1 1 11
## 15 1 -1 0
## 16 1 1 10
## 17 1 1 11
## 18 1 1 11
## 19 1 1 10
## 20 1 -1 0
## 21 1 -1 0
## 22 1 -1 0
## 23 1 -1 0
##
## $terms
## pca_data_2023 ~ microsite * shrub_density
## attr(,"variables")
## list(pca_data_2023, microsite, shrub_density)
## attr(,"factors")
## microsite shrub_density microsite:shrub_density
## pca_data_2023 0 0 0
## microsite 1 0 1
## shrub_density 0 1 1
## attr(,"term.labels")
## [1] "microsite" "shrub_density"
## [3] "microsite:shrub_density"
## attr(,"order")
## [1] 1 1 2
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
##
## attr(,"class")
## [1] "adonis"dist_2023 <- vegdist(pca_data_2023, species = "bray")
res_2023 <- pcoa(dist_2023)
p2 <- as.data.frame(res_2023$vectors)%>%
dplyr::select(Axis.1, Axis.2) %>%
bind_cols(env_2023,.)
ggplot(p2, aes(Axis.1, Axis.2, group = microsite)) +
geom_point(aes(color = microsite)) +
geom_text(aes(label=plot), hjust = 0, vjust = 0, check_overlap = TRUE, nudge_x = 0.01)+
scale_color_brewer(palette = "Set1") +
labs(color = "", subtitle = "labels denote plot identity")
model02 <- betadisper(dist_2023, env_2023$microsite)
model02##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_2023, group = env_2023$microsite)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 7
##
## Average distance to median:
## Density Open
## 0.4638 0.5348
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 22 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.0914 1.4576 1.0790 0.5277 0.4666 0.3903 0.3612 0.1835anova(model02)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 1 0.02894 0.028937 1.5275 0.2301
## Residuals 21 0.39782 0.018944permutest(model02,pairwise = TRUE, permutations = 99)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.02894 0.028937 1.5275 99 0.27
## Residuals 21 0.39782 0.018944
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Density Open
## Density 0.25
## Open 0.23013model02.HSD <- TukeyHSD(model02)
boxplot(model02)
model03 <- betadisper(dist_2023, env_2023$shrub_density)
model03##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_2023, group = env_2023$shrub_density)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 7
##
## Average distance to median:
## 0 10 11 12 13 14
## 0.5348 0.3051 0.4393 0.0000 0.0000 0.0000
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 22 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.0914 1.4576 1.0790 0.5277 0.4666 0.3903 0.3612 0.1835anova(model03)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 5 0.71375 0.142751 4.86 0.006083 **
## Residuals 17 0.49934 0.029373
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1permutest(model03,pairwise = TRUE, permutations = 99)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 5 0.71375 0.142751 4.86 99 0.02 *
## Residuals 17 0.49934 0.029373
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## 0 10 11 12 13 14
## 0 0.080000 0.230000
## 10 0.059818 0.460000
## 11 0.192393 0.435177
## 12
## 13
## 14model03.HSD <- TukeyHSD(model03)
boxplot(model03)
model04 <- betadisper(dist_2023, env_2023$site)
model04##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_2023, group = env_2023$site)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 7
##
## Average distance to median:
## Carrizo Cuyama Tecopa
## 0.5093 0.4594 0.4488
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 22 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.0914 1.4576 1.0790 0.5277 0.4666 0.3903 0.3612 0.1835anova(model04)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 2 0.01656 0.0082795 0.4577 0.6392
## Residuals 20 0.36181 0.0180906permutest(model04,pairwise = TRUE, permutations = 99)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 2 0.01656 0.0082795 0.4577 99 0.66
## Residuals 20 0.36181 0.0180906
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Carrizo Cuyama Tecopa
## Carrizo 0.60000 0.14
## Cuyama 0.56406 0.90
## Tecopa 0.14036 0.89377model04.HSD <- TukeyHSD(model04)
boxplot(model04)
### From running the community compositions both in 2022 and 2023 it
seems the community compositions across densities, microsites, and sites
are all somewhat similar with no significant differences.
Run PCOAs for both years combined!
photo_final <- read.csv("observations_final.csv")
summary(photo_final)## region site site_code microsite
## Length:250716 Length:250716 Length:250716 Length:250716
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## plot cam_ID month date
## Min. :1.000 Min. :1.000 Length:250716 Length:250716
## 1st Qu.:1.000 1st Qu.:1.000 Class :character Class :character
## Median :2.000 Median :1.000 Mode :character Mode :character
## Mean :1.772 Mean :1.498
## 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :4.000 Max. :2.000
## year shrub_density rep identified_by
## Min. :2022 Min. : 0.000 Min. : 1 Length:250716
## 1st Qu.:2023 1st Qu.: 0.000 1st Qu.: 21744 Class :character
## Median :2023 Median :10.000 Median : 67344 Mode :character
## Mean :2023 Mean : 5.878 Mean : 77566
## 3rd Qu.:2023 3rd Qu.:11.000 3rd Qu.:130022
## Max. :2023 Max. :14.000 Max. :192701
## filename timestamp animal.hit class
## Length:250716 Length:250716 Min. :0.00000 Length:250716
## Class :character Class :character 1st Qu.:0.00000 Class :character
## Mode :character Mode :character Median :0.00000 Mode :character
## Mean :0.01923
## 3rd Qu.:0.00000
## Max. :1.00000
## order family genus species
## Length:250716 Length:250716 Length:250716 Length:250716
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## common_name number_of_objects
## Length:250716 Min. : 1
## Class :character 1st Qu.: 1
## Mode :character Median : 1
## Mean : 1
## 3rd Qu.: 1
## Max. :12photo_final <- photo_final %>%
filter(common_name != "Human")
photo_final <- photo_final %>%
filter(common_name != "Human-Camera Trapper")
photo_final <- photo_final %>%
filter(common_name != "Domestic Dog")
photo_final <- photo_final %>%
filter(common_name != "Vehicle")
photo_final <- photo_final %>%
dplyr::filter(common_name != "Insect")
photo_final <- photo_final %>%
dplyr::filter(common_name != "Animal")
photo_final <- photo_final %>%
dplyr::filter(common_name != "Bird")
count.hit_final <- photo_final %>%
count(animal.hit) %>%
na.omit()
summary(count.hit_final)## animal.hit n
## Min. :0.00 Min. : 3717
## 1st Qu.:0.25 1st Qu.: 64261
## Median :0.50 Median :124806
## Mean :0.50 Mean :124806
## 3rd Qu.:0.75 3rd Qu.:185350
## Max. :1.00 Max. :245894# Wow we have a 1.489% capture rate for the project!### Animal Observations by Site_Code
animals_by_sitecode_final <- photo_final%>%
group_by(site_code, microsite, common_name) %>%
summarise(captures = sum(animal.hit), n = n())## `summarise()` has grouped output by 'site_code', 'microsite'. You can override
## using the `.groups` argument.animals_by_sitecode_final <- animals_by_sitecode_final %>%
filter(common_name != "Blank") %>% filter(common_name != "No CV Result") animals_by_site_final <- photo_final %>% group_by(site,microsite,common_name) %>% summarise(captures = sum(animal.hit))## `summarise()` has grouped output by 'site', 'microsite'. You can override using
## the `.groups` argument.animals_by_site_final <- animals_by_site_final %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")animals_by_density_final<- photo_final %>% group_by(microsite,common_name) %>% summarise(captures = sum(animal.hit))## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.animals_by_density_final <- animals_by_density_final %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")Total_Observations_final <- photo_final %>% group_by(common_name) %>% summarise(total = sum(animal.hit)) %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result") %>% filter(common_name != "Mammal")density_obvs_final <- merge(animals_by_density_final, Total_Observations_final, all = TRUE) %>% filter(common_name != "Mammal")
density_obvs_final$percent_presence <- density_obvs_final$captures/density_obvs_final$totalm3<- glm(total ~ microsite*common_name, family = "poisson", data = density_obvs_final)
anova(m3, test = "Chisq")## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: total
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 55 27821
## microsite 1 29.6 54 27791 5.449e-08 ***
## common_name 32 27791.1 22 0 < 2.2e-16 ***
## microsite:common_name 22 0.0 0 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e3 <- emmeans(m3, pairwise~common_name)## NOTE: Results may be misleading due to involvement in interactionse3## $emmeans
## common_name emmean SE df asymp.LCL asymp.UCL
## American Robin nonEst NA NA NA NA
## Black-tailed Jackrabbit 5.878 0.03742 Inf 5.8044 5.951
## Black-throated Sparrow nonEst NA NA NA NA
## Blunt-nosed Leopard Lizard 1.386 0.35355 Inf 0.6933 2.079
## Bobcat 1.792 0.28868 Inf 1.2260 2.358
## Brewer's Blackbird 2.833 0.17150 Inf 2.4971 3.169
## California Ground Squirrel 5.447 0.04642 Inf 5.3557 5.538
## California Pocket Mouse 1.386 0.35355 Inf 0.6933 2.079
## California Quail 3.258 0.13867 Inf 2.9863 3.530
## California Thrasher nonEst NA NA NA NA
## Common Raven 4.394 0.07857 Inf 4.2405 4.548
## Coyote 4.898 0.06108 Inf 4.7781 5.018
## Desert Cottontail 4.143 0.08909 Inf 3.9685 4.318
## Desert Iguana nonEst NA NA NA NA
## Giant Kangaroo Rat 4.220 0.08575 Inf 4.0514 4.388
## Great White Egret nonEst NA NA NA NA
## Greater Roadrunner 1.946 0.26726 Inf 1.4221 2.470
## Heermann's Kangaroo Rat 7.782 0.01444 Inf 7.7537 7.810
## Horned Lark nonEst NA NA NA NA
## Killdeer nonEst NA NA NA NA
## Kit Fox 2.708 0.18257 Inf 2.3502 3.066
## Lark Sparrow 1.946 0.26726 Inf 1.4221 2.470
## Loggerhead Shrike 2.565 0.19612 Inf 2.1806 2.949
## Merriam's Kangaroo Rat 2.565 0.19612 Inf 2.1806 2.949
## Mohave Ground Squirrel nonEst NA NA NA NA
## Mourning Dove 1.609 0.31623 Inf 0.9896 2.229
## Nelson's Antelope Squirrel 5.142 0.05407 Inf 5.0357 5.248
## Red-tailed Hawk nonEst NA NA NA NA
## Salinas Pocket Mouse 1.386 0.35355 Inf 0.6933 2.079
## Say's Phoebe 1.386 0.35355 Inf 0.6933 2.079
## Vesper Sparrow 2.565 0.19612 Inf 2.1806 2.949
## Western whiptail nonEst NA NA NA NA
## White-tailed Antelope Squirrel 1.792 0.28868 Inf 1.2260 2.358
##
## Results are averaged over the levels of: microsite
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate
## American Robin - (Black-tailed Jackrabbit) nonEst
## American Robin - (Black-throated Sparrow) nonEst
## American Robin - (Blunt-nosed Leopard Lizard) nonEst
## American Robin - Bobcat nonEst
## American Robin - Brewer's Blackbird nonEst
## American Robin - California Ground Squirrel nonEst
## American Robin - California Pocket Mouse nonEst
## American Robin - California Quail nonEst
## American Robin - California Thrasher nonEst
## American Robin - Common Raven nonEst
## American Robin - Coyote nonEst
## American Robin - Desert Cottontail nonEst
## American Robin - Desert Iguana nonEst
## American Robin - Giant Kangaroo Rat nonEst
## American Robin - Great White Egret nonEst
## American Robin - Greater Roadrunner nonEst
## American Robin - Heermann's Kangaroo Rat nonEst
## American Robin - Horned Lark nonEst
## American Robin - Killdeer nonEst
## American Robin - Kit Fox nonEst
## American Robin - Lark Sparrow nonEst
## American Robin - Loggerhead Shrike nonEst
## American Robin - Merriam's Kangaroo Rat nonEst
## American Robin - Mohave Ground Squirrel nonEst
## American Robin - Mourning Dove nonEst
## American Robin - Nelson's Antelope Squirrel nonEst
## American Robin - (Red-tailed Hawk) nonEst
## American Robin - Salinas Pocket Mouse nonEst
## American Robin - Say's Phoebe nonEst
## American Robin - Vesper Sparrow nonEst
## American Robin - Western whiptail nonEst
## American Robin - (White-tailed Antelope Squirrel) nonEst
## (Black-tailed Jackrabbit) - (Black-throated Sparrow) nonEst
## (Black-tailed Jackrabbit) - (Blunt-nosed Leopard Lizard) 4.4914
## (Black-tailed Jackrabbit) - Bobcat 4.0860
## (Black-tailed Jackrabbit) - Brewer's Blackbird 3.0445
## (Black-tailed Jackrabbit) - California Ground Squirrel 0.4310
## (Black-tailed Jackrabbit) - California Pocket Mouse 4.4914
## (Black-tailed Jackrabbit) - California Quail 2.6196
## (Black-tailed Jackrabbit) - California Thrasher nonEst
## (Black-tailed Jackrabbit) - Common Raven 1.4833
## (Black-tailed Jackrabbit) - Coyote 0.9799
## (Black-tailed Jackrabbit) - Desert Cottontail 1.7346
## (Black-tailed Jackrabbit) - Desert Iguana nonEst
## (Black-tailed Jackrabbit) - Giant Kangaroo Rat 1.6582
## (Black-tailed Jackrabbit) - Great White Egret nonEst
## (Black-tailed Jackrabbit) - Greater Roadrunner 3.9318
## (Black-tailed Jackrabbit) - Heermann's Kangaroo Rat -1.9042
## (Black-tailed Jackrabbit) - Horned Lark nonEst
## (Black-tailed Jackrabbit) - Killdeer nonEst
## (Black-tailed Jackrabbit) - Kit Fox 3.1697
## (Black-tailed Jackrabbit) - Lark Sparrow 3.9318
## (Black-tailed Jackrabbit) - Loggerhead Shrike 3.3128
## (Black-tailed Jackrabbit) - Merriam's Kangaroo Rat 3.3128
## (Black-tailed Jackrabbit) - Mohave Ground Squirrel nonEst
## (Black-tailed Jackrabbit) - Mourning Dove 4.2683
## (Black-tailed Jackrabbit) - Nelson's Antelope Squirrel 0.7361
## (Black-tailed Jackrabbit) - (Red-tailed Hawk) nonEst
## (Black-tailed Jackrabbit) - Salinas Pocket Mouse 4.4914
## (Black-tailed Jackrabbit) - Say's Phoebe 4.4914
## (Black-tailed Jackrabbit) - Vesper Sparrow 3.3128
## (Black-tailed Jackrabbit) - Western whiptail nonEst
## (Black-tailed Jackrabbit) - (White-tailed Antelope Squirrel) 4.0860
## (Black-throated Sparrow) - (Blunt-nosed Leopard Lizard) nonEst
## (Black-throated Sparrow) - Bobcat nonEst
## (Black-throated Sparrow) - Brewer's Blackbird nonEst
## (Black-throated Sparrow) - California Ground Squirrel nonEst
## (Black-throated Sparrow) - California Pocket Mouse nonEst
## (Black-throated Sparrow) - California Quail nonEst
## (Black-throated Sparrow) - California Thrasher nonEst
## (Black-throated Sparrow) - Common Raven nonEst
## (Black-throated Sparrow) - Coyote nonEst
## (Black-throated Sparrow) - Desert Cottontail nonEst
## (Black-throated Sparrow) - Desert Iguana nonEst
## (Black-throated Sparrow) - Giant Kangaroo Rat nonEst
## (Black-throated Sparrow) - Great White Egret nonEst
## (Black-throated Sparrow) - Greater Roadrunner nonEst
## (Black-throated Sparrow) - Heermann's Kangaroo Rat nonEst
## (Black-throated Sparrow) - Horned Lark nonEst
## (Black-throated Sparrow) - Killdeer nonEst
## (Black-throated Sparrow) - Kit Fox nonEst
## (Black-throated Sparrow) - Lark Sparrow nonEst
## (Black-throated Sparrow) - Loggerhead Shrike nonEst
## (Black-throated Sparrow) - Merriam's Kangaroo Rat nonEst
## (Black-throated Sparrow) - Mohave Ground Squirrel nonEst
## (Black-throated Sparrow) - Mourning Dove nonEst
## (Black-throated Sparrow) - Nelson's Antelope Squirrel nonEst
## (Black-throated Sparrow) - (Red-tailed Hawk) nonEst
## (Black-throated Sparrow) - Salinas Pocket Mouse nonEst
## (Black-throated Sparrow) - Say's Phoebe nonEst
## (Black-throated Sparrow) - Vesper Sparrow nonEst
## (Black-throated Sparrow) - Western whiptail nonEst
## (Black-throated Sparrow) - (White-tailed Antelope Squirrel) nonEst
## (Blunt-nosed Leopard Lizard) - Bobcat -0.4055
## (Blunt-nosed Leopard Lizard) - Brewer's Blackbird -1.4469
## (Blunt-nosed Leopard Lizard) - California Ground Squirrel -4.0604
## (Blunt-nosed Leopard Lizard) - California Pocket Mouse 0.0000
## (Blunt-nosed Leopard Lizard) - California Quail -1.8718
## (Blunt-nosed Leopard Lizard) - California Thrasher nonEst
## (Blunt-nosed Leopard Lizard) - Common Raven -3.0082
## (Blunt-nosed Leopard Lizard) - Coyote -3.5115
## (Blunt-nosed Leopard Lizard) - Desert Cottontail -2.7568
## (Blunt-nosed Leopard Lizard) - Desert Iguana nonEst
## (Blunt-nosed Leopard Lizard) - Giant Kangaroo Rat -2.8332
## (Blunt-nosed Leopard Lizard) - Great White Egret nonEst
## (Blunt-nosed Leopard Lizard) - Greater Roadrunner -0.5596
## (Blunt-nosed Leopard Lizard) - Heermann's Kangaroo Rat -6.3957
## (Blunt-nosed Leopard Lizard) - Horned Lark nonEst
## (Blunt-nosed Leopard Lizard) - Killdeer nonEst
## (Blunt-nosed Leopard Lizard) - Kit Fox -1.3218
## (Blunt-nosed Leopard Lizard) - Lark Sparrow -0.5596
## (Blunt-nosed Leopard Lizard) - Loggerhead Shrike -1.1787
## (Blunt-nosed Leopard Lizard) - Merriam's Kangaroo Rat -1.1787
## (Blunt-nosed Leopard Lizard) - Mohave Ground Squirrel nonEst
## (Blunt-nosed Leopard Lizard) - Mourning Dove -0.2231
## (Blunt-nosed Leopard Lizard) - Nelson's Antelope Squirrel -3.7554
## (Blunt-nosed Leopard Lizard) - (Red-tailed Hawk) nonEst
## (Blunt-nosed Leopard Lizard) - Salinas Pocket Mouse 0.0000
## (Blunt-nosed Leopard Lizard) - Say's Phoebe 0.0000
## (Blunt-nosed Leopard Lizard) - Vesper Sparrow -1.1787
## (Blunt-nosed Leopard Lizard) - Western whiptail nonEst
## (Blunt-nosed Leopard Lizard) - (White-tailed Antelope Squirrel) -0.4055
## Bobcat - Brewer's Blackbird -1.0415
## Bobcat - California Ground Squirrel -3.6550
## Bobcat - California Pocket Mouse 0.4055
## Bobcat - California Quail -1.4663
## Bobcat - California Thrasher nonEst
## Bobcat - Common Raven -2.6027
## Bobcat - Coyote -3.1061
## Bobcat - Desert Cottontail -2.3514
## Bobcat - Desert Iguana nonEst
## Bobcat - Giant Kangaroo Rat -2.4277
## Bobcat - Great White Egret nonEst
## Bobcat - Greater Roadrunner -0.1542
## Bobcat - Heermann's Kangaroo Rat -5.9902
## Bobcat - Horned Lark nonEst
## Bobcat - Killdeer nonEst
## Bobcat - Kit Fox -0.9163
## Bobcat - Lark Sparrow -0.1542
## Bobcat - Loggerhead Shrike -0.7732
## Bobcat - Merriam's Kangaroo Rat -0.7732
## Bobcat - Mohave Ground Squirrel nonEst
## Bobcat - Mourning Dove 0.1823
## Bobcat - Nelson's Antelope Squirrel -3.3499
## Bobcat - (Red-tailed Hawk) nonEst
## Bobcat - Salinas Pocket Mouse 0.4055
## Bobcat - Say's Phoebe 0.4055
## Bobcat - Vesper Sparrow -0.7732
## Bobcat - Western whiptail nonEst
## Bobcat - (White-tailed Antelope Squirrel) 0.0000
## Brewer's Blackbird - California Ground Squirrel -2.6135
## Brewer's Blackbird - California Pocket Mouse 1.4469
## Brewer's Blackbird - California Quail -0.4249
## Brewer's Blackbird - California Thrasher nonEst
## Brewer's Blackbird - Common Raven -1.5612
## Brewer's Blackbird - Coyote -2.0646
## Brewer's Blackbird - Desert Cottontail -1.3099
## Brewer's Blackbird - Desert Iguana nonEst
## Brewer's Blackbird - Giant Kangaroo Rat -1.3863
## Brewer's Blackbird - Great White Egret nonEst
## Brewer's Blackbird - Greater Roadrunner 0.8873
## Brewer's Blackbird - Heermann's Kangaroo Rat -4.9488
## Brewer's Blackbird - Horned Lark nonEst
## Brewer's Blackbird - Killdeer nonEst
## Brewer's Blackbird - Kit Fox 0.1252
## Brewer's Blackbird - Lark Sparrow 0.8873
## Brewer's Blackbird - Loggerhead Shrike 0.2683
## Brewer's Blackbird - Merriam's Kangaroo Rat 0.2683
## Brewer's Blackbird - Mohave Ground Squirrel nonEst
## Brewer's Blackbird - Mourning Dove 1.2238
## Brewer's Blackbird - Nelson's Antelope Squirrel -2.3085
## Brewer's Blackbird - (Red-tailed Hawk) nonEst
## Brewer's Blackbird - Salinas Pocket Mouse 1.4469
## Brewer's Blackbird - Say's Phoebe 1.4469
## Brewer's Blackbird - Vesper Sparrow 0.2683
## Brewer's Blackbird - Western whiptail nonEst
## Brewer's Blackbird - (White-tailed Antelope Squirrel) 1.0415
## California Ground Squirrel - California Pocket Mouse 4.0604
## California Ground Squirrel - California Quail 2.1886
## California Ground Squirrel - California Thrasher nonEst
## California Ground Squirrel - Common Raven 1.0523
## California Ground Squirrel - Coyote 0.5489
## California Ground Squirrel - Desert Cottontail 1.3036
## California Ground Squirrel - Desert Iguana nonEst
## California Ground Squirrel - Giant Kangaroo Rat 1.2272
## California Ground Squirrel - Great White Egret nonEst
## California Ground Squirrel - Greater Roadrunner 3.5008
## California Ground Squirrel - Heermann's Kangaroo Rat -2.3352
## California Ground Squirrel - Horned Lark nonEst
## California Ground Squirrel - Killdeer nonEst
## California Ground Squirrel - Kit Fox 2.7387
## California Ground Squirrel - Lark Sparrow 3.5008
## California Ground Squirrel - Loggerhead Shrike 2.8818
## California Ground Squirrel - Merriam's Kangaroo Rat 2.8818
## California Ground Squirrel - Mohave Ground Squirrel nonEst
## California Ground Squirrel - Mourning Dove 3.8373
## California Ground Squirrel - Nelson's Antelope Squirrel 0.3051
## California Ground Squirrel - (Red-tailed Hawk) nonEst
## California Ground Squirrel - Salinas Pocket Mouse 4.0604
## California Ground Squirrel - Say's Phoebe 4.0604
## California Ground Squirrel - Vesper Sparrow 2.8818
## California Ground Squirrel - Western whiptail nonEst
## California Ground Squirrel - (White-tailed Antelope Squirrel) 3.6550
## California Pocket Mouse - California Quail -1.8718
## California Pocket Mouse - California Thrasher nonEst
## California Pocket Mouse - Common Raven -3.0082
## California Pocket Mouse - Coyote -3.5115
## California Pocket Mouse - Desert Cottontail -2.7568
## California Pocket Mouse - Desert Iguana nonEst
## California Pocket Mouse - Giant Kangaroo Rat -2.8332
## California Pocket Mouse - Great White Egret nonEst
## California Pocket Mouse - Greater Roadrunner -0.5596
## California Pocket Mouse - Heermann's Kangaroo Rat -6.3957
## California Pocket Mouse - Horned Lark nonEst
## California Pocket Mouse - Killdeer nonEst
## California Pocket Mouse - Kit Fox -1.3218
## California Pocket Mouse - Lark Sparrow -0.5596
## California Pocket Mouse - Loggerhead Shrike -1.1787
## California Pocket Mouse - Merriam's Kangaroo Rat -1.1787
## California Pocket Mouse - Mohave Ground Squirrel nonEst
## California Pocket Mouse - Mourning Dove -0.2231
## California Pocket Mouse - Nelson's Antelope Squirrel -3.7554
## California Pocket Mouse - (Red-tailed Hawk) nonEst
## California Pocket Mouse - Salinas Pocket Mouse 0.0000
## California Pocket Mouse - Say's Phoebe 0.0000
## California Pocket Mouse - Vesper Sparrow -1.1787
## California Pocket Mouse - Western whiptail nonEst
## California Pocket Mouse - (White-tailed Antelope Squirrel) -0.4055
## California Quail - California Thrasher nonEst
## California Quail - Common Raven -1.1364
## California Quail - Coyote -1.6397
## California Quail - Desert Cottontail -0.8850
## California Quail - Desert Iguana nonEst
## California Quail - Giant Kangaroo Rat -0.9614
## California Quail - Great White Egret nonEst
## California Quail - Greater Roadrunner 1.3122
## California Quail - Heermann's Kangaroo Rat -4.5239
## California Quail - Horned Lark nonEst
## California Quail - Killdeer nonEst
## California Quail - Kit Fox 0.5500
## California Quail - Lark Sparrow 1.3122
## California Quail - Loggerhead Shrike 0.6931
## California Quail - Merriam's Kangaroo Rat 0.6931
## California Quail - Mohave Ground Squirrel nonEst
## California Quail - Mourning Dove 1.6487
## California Quail - Nelson's Antelope Squirrel -1.8836
## California Quail - (Red-tailed Hawk) nonEst
## California Quail - Salinas Pocket Mouse 1.8718
## California Quail - Say's Phoebe 1.8718
## California Quail - Vesper Sparrow 0.6931
## California Quail - Western whiptail nonEst
## California Quail - (White-tailed Antelope Squirrel) 1.4663
## California Thrasher - Common Raven nonEst
## California Thrasher - Coyote nonEst
## California Thrasher - Desert Cottontail nonEst
## California Thrasher - Desert Iguana nonEst
## California Thrasher - Giant Kangaroo Rat nonEst
## California Thrasher - Great White Egret nonEst
## California Thrasher - Greater Roadrunner nonEst
## California Thrasher - Heermann's Kangaroo Rat nonEst
## California Thrasher - Horned Lark nonEst
## California Thrasher - Killdeer nonEst
## California Thrasher - Kit Fox nonEst
## California Thrasher - Lark Sparrow nonEst
## California Thrasher - Loggerhead Shrike nonEst
## California Thrasher - Merriam's Kangaroo Rat nonEst
## California Thrasher - Mohave Ground Squirrel nonEst
## California Thrasher - Mourning Dove nonEst
## California Thrasher - Nelson's Antelope Squirrel nonEst
## California Thrasher - (Red-tailed Hawk) nonEst
## California Thrasher - Salinas Pocket Mouse nonEst
## California Thrasher - Say's Phoebe nonEst
## California Thrasher - Vesper Sparrow nonEst
## California Thrasher - Western whiptail nonEst
## California Thrasher - (White-tailed Antelope Squirrel) nonEst
## Common Raven - Coyote -0.5034
## Common Raven - Desert Cottontail 0.2513
## Common Raven - Desert Iguana nonEst
## Common Raven - Giant Kangaroo Rat 0.1749
## Common Raven - Great White Egret nonEst
## Common Raven - Greater Roadrunner 2.4485
## Common Raven - Heermann's Kangaroo Rat -3.3875
## Common Raven - Horned Lark nonEst
## Common Raven - Killdeer nonEst
## Common Raven - Kit Fox 1.6864
## Common Raven - Lark Sparrow 2.4485
## Common Raven - Loggerhead Shrike 1.8295
## Common Raven - Merriam's Kangaroo Rat 1.8295
## Common Raven - Mohave Ground Squirrel nonEst
## Common Raven - Mourning Dove 2.7850
## Common Raven - Nelson's Antelope Squirrel -0.7472
## Common Raven - (Red-tailed Hawk) nonEst
## Common Raven - Salinas Pocket Mouse 3.0082
## Common Raven - Say's Phoebe 3.0082
## Common Raven - Vesper Sparrow 1.8295
## Common Raven - Western whiptail nonEst
## Common Raven - (White-tailed Antelope Squirrel) 2.6027
## Coyote - Desert Cottontail 0.7547
## Coyote - Desert Iguana nonEst
## Coyote - Giant Kangaroo Rat 0.6783
## Coyote - Great White Egret nonEst
## Coyote - Greater Roadrunner 2.9519
## Coyote - Heermann's Kangaroo Rat -2.8841
## Coyote - Horned Lark nonEst
## Coyote - Killdeer nonEst
## Coyote - Kit Fox 2.1898
## Coyote - Lark Sparrow 2.9519
## Coyote - Loggerhead Shrike 2.3329
## Coyote - Merriam's Kangaroo Rat 2.3329
## Coyote - Mohave Ground Squirrel nonEst
## Coyote - Mourning Dove 3.2884
## Coyote - Nelson's Antelope Squirrel -0.2438
## Coyote - (Red-tailed Hawk) nonEst
## Coyote - Salinas Pocket Mouse 3.5115
## Coyote - Say's Phoebe 3.5115
## Coyote - Vesper Sparrow 2.3329
## Coyote - Western whiptail nonEst
## Coyote - (White-tailed Antelope Squirrel) 3.1061
## Desert Cottontail - Desert Iguana nonEst
## Desert Cottontail - Giant Kangaroo Rat -0.0764
## Desert Cottontail - Great White Egret nonEst
## Desert Cottontail - Greater Roadrunner 2.1972
## Desert Cottontail - Heermann's Kangaroo Rat -3.6388
## Desert Cottontail - Horned Lark nonEst
## Desert Cottontail - Killdeer nonEst
## Desert Cottontail - Kit Fox 1.4351
## Desert Cottontail - Lark Sparrow 2.1972
## Desert Cottontail - Loggerhead Shrike 1.5782
## Desert Cottontail - Merriam's Kangaroo Rat 1.5782
## Desert Cottontail - Mohave Ground Squirrel nonEst
## Desert Cottontail - Mourning Dove 2.5337
## Desert Cottontail - Nelson's Antelope Squirrel -0.9985
## Desert Cottontail - (Red-tailed Hawk) nonEst
## Desert Cottontail - Salinas Pocket Mouse 2.7568
## Desert Cottontail - Say's Phoebe 2.7568
## Desert Cottontail - Vesper Sparrow 1.5782
## Desert Cottontail - Western whiptail nonEst
## Desert Cottontail - (White-tailed Antelope Squirrel) 2.3514
## Desert Iguana - Giant Kangaroo Rat nonEst
## Desert Iguana - Great White Egret nonEst
## Desert Iguana - Greater Roadrunner nonEst
## Desert Iguana - Heermann's Kangaroo Rat nonEst
## Desert Iguana - Horned Lark nonEst
## Desert Iguana - Killdeer nonEst
## Desert Iguana - Kit Fox nonEst
## Desert Iguana - Lark Sparrow nonEst
## Desert Iguana - Loggerhead Shrike nonEst
## Desert Iguana - Merriam's Kangaroo Rat nonEst
## Desert Iguana - Mohave Ground Squirrel nonEst
## Desert Iguana - Mourning Dove nonEst
## Desert Iguana - Nelson's Antelope Squirrel nonEst
## Desert Iguana - (Red-tailed Hawk) nonEst
## Desert Iguana - Salinas Pocket Mouse nonEst
## Desert Iguana - Say's Phoebe nonEst
## Desert Iguana - Vesper Sparrow nonEst
## Desert Iguana - Western whiptail nonEst
## Desert Iguana - (White-tailed Antelope Squirrel) nonEst
## Giant Kangaroo Rat - Great White Egret nonEst
## Giant Kangaroo Rat - Greater Roadrunner 2.2736
## Giant Kangaroo Rat - Heermann's Kangaroo Rat -3.5625
## Giant Kangaroo Rat - Horned Lark nonEst
## Giant Kangaroo Rat - Killdeer nonEst
## Giant Kangaroo Rat - Kit Fox 1.5115
## Giant Kangaroo Rat - Lark Sparrow 2.2736
## Giant Kangaroo Rat - Loggerhead Shrike 1.6546
## Giant Kangaroo Rat - Merriam's Kangaroo Rat 1.6546
## Giant Kangaroo Rat - Mohave Ground Squirrel nonEst
## Giant Kangaroo Rat - Mourning Dove 2.6101
## Giant Kangaroo Rat - Nelson's Antelope Squirrel -0.9222
## Giant Kangaroo Rat - (Red-tailed Hawk) nonEst
## Giant Kangaroo Rat - Salinas Pocket Mouse 2.8332
## Giant Kangaroo Rat - Say's Phoebe 2.8332
## Giant Kangaroo Rat - Vesper Sparrow 1.6546
## Giant Kangaroo Rat - Western whiptail nonEst
## Giant Kangaroo Rat - (White-tailed Antelope Squirrel) 2.4277
## Great White Egret - Greater Roadrunner nonEst
## Great White Egret - Heermann's Kangaroo Rat nonEst
## Great White Egret - Horned Lark nonEst
## Great White Egret - Killdeer nonEst
## Great White Egret - Kit Fox nonEst
## Great White Egret - Lark Sparrow nonEst
## Great White Egret - Loggerhead Shrike nonEst
## Great White Egret - Merriam's Kangaroo Rat nonEst
## Great White Egret - Mohave Ground Squirrel nonEst
## Great White Egret - Mourning Dove nonEst
## Great White Egret - Nelson's Antelope Squirrel nonEst
## Great White Egret - (Red-tailed Hawk) nonEst
## Great White Egret - Salinas Pocket Mouse nonEst
## Great White Egret - Say's Phoebe nonEst
## Great White Egret - Vesper Sparrow nonEst
## Great White Egret - Western whiptail nonEst
## Great White Egret - (White-tailed Antelope Squirrel) nonEst
## Greater Roadrunner - Heermann's Kangaroo Rat -5.8361
## Greater Roadrunner - Horned Lark nonEst
## Greater Roadrunner - Killdeer nonEst
## Greater Roadrunner - Kit Fox -0.7621
## Greater Roadrunner - Lark Sparrow 0.0000
## Greater Roadrunner - Loggerhead Shrike -0.6190
## Greater Roadrunner - Merriam's Kangaroo Rat -0.6190
## Greater Roadrunner - Mohave Ground Squirrel nonEst
## Greater Roadrunner - Mourning Dove 0.3365
## Greater Roadrunner - Nelson's Antelope Squirrel -3.1958
## Greater Roadrunner - (Red-tailed Hawk) nonEst
## Greater Roadrunner - Salinas Pocket Mouse 0.5596
## Greater Roadrunner - Say's Phoebe 0.5596
## Greater Roadrunner - Vesper Sparrow -0.6190
## Greater Roadrunner - Western whiptail nonEst
## Greater Roadrunner - (White-tailed Antelope Squirrel) 0.1542
## Heermann's Kangaroo Rat - Horned Lark nonEst
## Heermann's Kangaroo Rat - Killdeer nonEst
## Heermann's Kangaroo Rat - Kit Fox 5.0739
## Heermann's Kangaroo Rat - Lark Sparrow 5.8361
## Heermann's Kangaroo Rat - Loggerhead Shrike 5.2170
## Heermann's Kangaroo Rat - Merriam's Kangaroo Rat 5.2170
## Heermann's Kangaroo Rat - Mohave Ground Squirrel nonEst
## Heermann's Kangaroo Rat - Mourning Dove 6.1725
## Heermann's Kangaroo Rat - Nelson's Antelope Squirrel 2.6403
## Heermann's Kangaroo Rat - (Red-tailed Hawk) nonEst
## Heermann's Kangaroo Rat - Salinas Pocket Mouse 6.3957
## Heermann's Kangaroo Rat - Say's Phoebe 6.3957
## Heermann's Kangaroo Rat - Vesper Sparrow 5.2170
## Heermann's Kangaroo Rat - Western whiptail nonEst
## Heermann's Kangaroo Rat - (White-tailed Antelope Squirrel) 5.9902
## Horned Lark - Killdeer nonEst
## Horned Lark - Kit Fox nonEst
## Horned Lark - Lark Sparrow nonEst
## Horned Lark - Loggerhead Shrike nonEst
## Horned Lark - Merriam's Kangaroo Rat nonEst
## Horned Lark - Mohave Ground Squirrel nonEst
## Horned Lark - Mourning Dove nonEst
## Horned Lark - Nelson's Antelope Squirrel nonEst
## Horned Lark - (Red-tailed Hawk) nonEst
## Horned Lark - Salinas Pocket Mouse nonEst
## Horned Lark - Say's Phoebe nonEst
## Horned Lark - Vesper Sparrow nonEst
## Horned Lark - Western whiptail nonEst
## Horned Lark - (White-tailed Antelope Squirrel) nonEst
## Killdeer - Kit Fox nonEst
## Killdeer - Lark Sparrow nonEst
## Killdeer - Loggerhead Shrike nonEst
## Killdeer - Merriam's Kangaroo Rat nonEst
## Killdeer - Mohave Ground Squirrel nonEst
## Killdeer - Mourning Dove nonEst
## Killdeer - Nelson's Antelope Squirrel nonEst
## Killdeer - (Red-tailed Hawk) nonEst
## Killdeer - Salinas Pocket Mouse nonEst
## Killdeer - Say's Phoebe nonEst
## Killdeer - Vesper Sparrow nonEst
## Killdeer - Western whiptail nonEst
## Killdeer - (White-tailed Antelope Squirrel) nonEst
## Kit Fox - Lark Sparrow 0.7621
## Kit Fox - Loggerhead Shrike 0.1431
## Kit Fox - Merriam's Kangaroo Rat 0.1431
## Kit Fox - Mohave Ground Squirrel nonEst
## Kit Fox - Mourning Dove 1.0986
## Kit Fox - Nelson's Antelope Squirrel -2.4336
## Kit Fox - (Red-tailed Hawk) nonEst
## Kit Fox - Salinas Pocket Mouse 1.3218
## Kit Fox - Say's Phoebe 1.3218
## Kit Fox - Vesper Sparrow 0.1431
## Kit Fox - Western whiptail nonEst
## Kit Fox - (White-tailed Antelope Squirrel) 0.9163
## Lark Sparrow - Loggerhead Shrike -0.6190
## Lark Sparrow - Merriam's Kangaroo Rat -0.6190
## Lark Sparrow - Mohave Ground Squirrel nonEst
## Lark Sparrow - Mourning Dove 0.3365
## Lark Sparrow - Nelson's Antelope Squirrel -3.1958
## Lark Sparrow - (Red-tailed Hawk) nonEst
## Lark Sparrow - Salinas Pocket Mouse 0.5596
## Lark Sparrow - Say's Phoebe 0.5596
## Lark Sparrow - Vesper Sparrow -0.6190
## Lark Sparrow - Western whiptail nonEst
## Lark Sparrow - (White-tailed Antelope Squirrel) 0.1542
## Loggerhead Shrike - Merriam's Kangaroo Rat 0.0000
## Loggerhead Shrike - Mohave Ground Squirrel nonEst
## Loggerhead Shrike - Mourning Dove 0.9555
## Loggerhead Shrike - Nelson's Antelope Squirrel -2.5767
## Loggerhead Shrike - (Red-tailed Hawk) nonEst
## Loggerhead Shrike - Salinas Pocket Mouse 1.1787
## Loggerhead Shrike - Say's Phoebe 1.1787
## Loggerhead Shrike - Vesper Sparrow 0.0000
## Loggerhead Shrike - Western whiptail nonEst
## Loggerhead Shrike - (White-tailed Antelope Squirrel) 0.7732
## Merriam's Kangaroo Rat - Mohave Ground Squirrel nonEst
## Merriam's Kangaroo Rat - Mourning Dove 0.9555
## Merriam's Kangaroo Rat - Nelson's Antelope Squirrel -2.5767
## Merriam's Kangaroo Rat - (Red-tailed Hawk) nonEst
## Merriam's Kangaroo Rat - Salinas Pocket Mouse 1.1787
## Merriam's Kangaroo Rat - Say's Phoebe 1.1787
## Merriam's Kangaroo Rat - Vesper Sparrow 0.0000
## Merriam's Kangaroo Rat - Western whiptail nonEst
## Merriam's Kangaroo Rat - (White-tailed Antelope Squirrel) 0.7732
## Mohave Ground Squirrel - Mourning Dove nonEst
## Mohave Ground Squirrel - Nelson's Antelope Squirrel nonEst
## Mohave Ground Squirrel - (Red-tailed Hawk) nonEst
## Mohave Ground Squirrel - Salinas Pocket Mouse nonEst
## Mohave Ground Squirrel - Say's Phoebe nonEst
## Mohave Ground Squirrel - Vesper Sparrow nonEst
## Mohave Ground Squirrel - Western whiptail nonEst
## Mohave Ground Squirrel - (White-tailed Antelope Squirrel) nonEst
## Mourning Dove - Nelson's Antelope Squirrel -3.5322
## Mourning Dove - (Red-tailed Hawk) nonEst
## Mourning Dove - Salinas Pocket Mouse 0.2231
## Mourning Dove - Say's Phoebe 0.2231
## Mourning Dove - Vesper Sparrow -0.9555
## Mourning Dove - Western whiptail nonEst
## Mourning Dove - (White-tailed Antelope Squirrel) -0.1823
## Nelson's Antelope Squirrel - (Red-tailed Hawk) nonEst
## Nelson's Antelope Squirrel - Salinas Pocket Mouse 3.7554
## Nelson's Antelope Squirrel - Say's Phoebe 3.7554
## Nelson's Antelope Squirrel - Vesper Sparrow 2.5767
## Nelson's Antelope Squirrel - Western whiptail nonEst
## Nelson's Antelope Squirrel - (White-tailed Antelope Squirrel) 3.3499
## (Red-tailed Hawk) - Salinas Pocket Mouse nonEst
## (Red-tailed Hawk) - Say's Phoebe nonEst
## (Red-tailed Hawk) - Vesper Sparrow nonEst
## (Red-tailed Hawk) - Western whiptail nonEst
## (Red-tailed Hawk) - (White-tailed Antelope Squirrel) nonEst
## Salinas Pocket Mouse - Say's Phoebe 0.0000
## Salinas Pocket Mouse - Vesper Sparrow -1.1787
## Salinas Pocket Mouse - Western whiptail nonEst
## Salinas Pocket Mouse - (White-tailed Antelope Squirrel) -0.4055
## Say's Phoebe - Vesper Sparrow -1.1787
## Say's Phoebe - Western whiptail nonEst
## Say's Phoebe - (White-tailed Antelope Squirrel) -0.4055
## Vesper Sparrow - Western whiptail nonEst
## Vesper Sparrow - (White-tailed Antelope Squirrel) 0.7732
## Western whiptail - (White-tailed Antelope Squirrel) nonEst
## SE df z.ratio p.value
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.3555 Inf 12.633 <.0001
## 0.2911 Inf 14.037 <.0001
## 0.1755 Inf 17.344 <.0001
## 0.0596 Inf 7.228 <.0001
## 0.3555 Inf 12.633 <.0001
## 0.1436 Inf 18.238 <.0001
## NA NA NA NA
## 0.0870 Inf 17.044 <.0001
## 0.0716 Inf 13.679 <.0001
## 0.0966 Inf 17.951 <.0001
## NA NA NA NA
## 0.0936 Inf 17.724 <.0001
## NA NA NA NA
## 0.2699 Inf 14.569 <.0001
## 0.0401 Inf -47.470 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.1864 Inf 17.007 <.0001
## 0.2699 Inf 14.569 <.0001
## 0.1997 Inf 16.593 <.0001
## 0.1997 Inf 16.593 <.0001
## NA NA NA NA
## 0.3184 Inf 13.404 <.0001
## 0.0658 Inf 11.193 <.0001
## NA NA NA NA
## 0.3555 Inf 12.633 <.0001
## 0.3555 Inf 12.633 <.0001
## 0.1997 Inf 16.593 <.0001
## NA NA NA NA
## 0.2911 Inf 14.037 <.0001
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.4564 Inf -0.888 1.0000
## 0.3930 Inf -3.682 0.0729
## 0.3566 Inf -11.387 <.0001
## 0.5000 Inf 0.000 1.0000
## 0.3798 Inf -4.929 0.0004
## NA NA NA NA
## 0.3622 Inf -8.306 <.0001
## 0.3588 Inf -9.787 <.0001
## 0.3646 Inf -7.561 <.0001
## NA NA NA NA
## 0.3638 Inf -7.788 <.0001
## NA NA NA NA
## 0.4432 Inf -1.263 1.0000
## 0.3538 Inf -18.075 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.3979 Inf -3.322 0.2085
## 0.4432 Inf -1.263 1.0000
## 0.4043 Inf -2.915 0.4964
## 0.4043 Inf -2.915 0.4964
## NA NA NA NA
## 0.4743 Inf -0.470 1.0000
## 0.3577 Inf -10.500 <.0001
## NA NA NA NA
## 0.5000 Inf 0.000 1.0000
## 0.5000 Inf 0.000 1.0000
## 0.4043 Inf -2.915 0.4964
## NA NA NA NA
## 0.4564 Inf -0.888 1.0000
## 0.3358 Inf -3.102 0.3488
## 0.2924 Inf -12.501 <.0001
## 0.4564 Inf 0.888 1.0000
## 0.3203 Inf -4.579 0.0021
## NA NA NA NA
## 0.2992 Inf -8.700 <.0001
## 0.2951 Inf -10.527 <.0001
## 0.3021 Inf -7.783 <.0001
## NA NA NA NA
## 0.3011 Inf -8.062 <.0001
## NA NA NA NA
## 0.3934 Inf -0.392 1.0000
## 0.2890 Inf -20.725 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.3416 Inf -2.683 0.6900
## 0.3934 Inf -0.392 1.0000
## 0.3490 Inf -2.216 0.9487
## 0.3490 Inf -2.216 0.9487
## NA NA NA NA
## 0.4282 Inf 0.426 1.0000
## 0.2937 Inf -11.406 <.0001
## NA NA NA NA
## 0.4564 Inf 0.888 1.0000
## 0.4564 Inf 0.888 1.0000
## 0.3490 Inf -2.216 0.9487
## NA NA NA NA
## 0.4082 Inf 0.000 1.0000
## 0.1777 Inf -14.710 <.0001
## 0.3930 Inf 3.682 0.0729
## 0.2206 Inf -1.926 0.9926
## NA NA NA NA
## 0.1886 Inf -8.276 <.0001
## 0.1821 Inf -11.341 <.0001
## 0.1933 Inf -6.778 <.0001
## NA NA NA NA
## 0.1917 Inf -7.230 <.0001
## NA NA NA NA
## 0.3176 Inf 2.794 0.5984
## 0.1721 Inf -28.754 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.2505 Inf 0.500 1.0000
## 0.3176 Inf 2.794 0.5984
## 0.2605 Inf 1.030 1.0000
## 0.2605 Inf 1.030 1.0000
## NA NA NA NA
## 0.3597 Inf 3.402 0.1687
## 0.1798 Inf -12.837 <.0001
## NA NA NA NA
## 0.3930 Inf 3.682 0.0729
## 0.3930 Inf 3.682 0.0729
## 0.2605 Inf 1.030 1.0000
## NA NA NA NA
## 0.3358 Inf 3.102 0.3488
## 0.3566 Inf 11.387 <.0001
## 0.1462 Inf 14.966 <.0001
## NA NA NA NA
## 0.0913 Inf 11.531 <.0001
## 0.0767 Inf 7.154 <.0001
## 0.1005 Inf 12.977 <.0001
## NA NA NA NA
## 0.0975 Inf 12.586 <.0001
## NA NA NA NA
## 0.2713 Inf 12.906 <.0001
## 0.0486 Inf -48.032 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.1884 Inf 14.538 <.0001
## 0.2713 Inf 12.906 <.0001
## 0.2015 Inf 14.299 <.0001
## 0.2015 Inf 14.299 <.0001
## NA NA NA NA
## 0.3196 Inf 12.006 <.0001
## 0.0713 Inf 4.281 0.0079
## NA NA NA NA
## 0.3566 Inf 11.387 <.0001
## 0.3566 Inf 11.387 <.0001
## 0.2015 Inf 14.299 <.0001
## NA NA NA NA
## 0.2924 Inf 12.501 <.0001
## 0.3798 Inf -4.929 0.0004
## NA NA NA NA
## 0.3622 Inf -8.306 <.0001
## 0.3588 Inf -9.787 <.0001
## 0.3646 Inf -7.561 <.0001
## NA NA NA NA
## 0.3638 Inf -7.788 <.0001
## NA NA NA NA
## 0.4432 Inf -1.263 1.0000
## 0.3538 Inf -18.075 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.3979 Inf -3.322 0.2085
## 0.4432 Inf -1.263 1.0000
## 0.4043 Inf -2.915 0.4964
## 0.4043 Inf -2.915 0.4964
## NA NA NA NA
## 0.4743 Inf -0.470 1.0000
## 0.3577 Inf -10.500 <.0001
## NA NA NA NA
## 0.5000 Inf 0.000 1.0000
## 0.5000 Inf 0.000 1.0000
## 0.4043 Inf -2.915 0.4964
## NA NA NA NA
## 0.4564 Inf -0.888 1.0000
## NA NA NA NA
## 0.1594 Inf -7.130 <.0001
## 0.1515 Inf -10.821 <.0001
## 0.1648 Inf -5.370 <.0001
## NA NA NA NA
## 0.1630 Inf -5.897 <.0001
## NA NA NA NA
## 0.3011 Inf 4.358 0.0057
## 0.1394 Inf -32.447 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.2293 Inf 2.399 0.8778
## 0.3011 Inf 4.358 0.0057
## 0.2402 Inf 2.886 0.5211
## 0.2402 Inf 2.886 0.5211
## NA NA NA NA
## 0.3453 Inf 4.775 0.0009
## 0.1488 Inf -12.655 <.0001
## NA NA NA NA
## 0.3798 Inf 4.929 0.0004
## 0.3798 Inf 4.929 0.0004
## 0.2402 Inf 2.886 0.5211
## NA NA NA NA
## 0.3203 Inf 4.579 0.0021
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.0995 Inf -5.058 0.0002
## 0.1188 Inf 2.116 0.9713
## NA NA NA NA
## 0.1163 Inf 1.504 0.9999
## NA NA NA NA
## 0.2786 Inf 8.790 <.0001
## 0.0799 Inf -42.406 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.1988 Inf 8.485 <.0001
## 0.2786 Inf 8.790 <.0001
## 0.2113 Inf 8.660 <.0001
## 0.2113 Inf 8.660 <.0001
## NA NA NA NA
## 0.3258 Inf 8.547 <.0001
## 0.0954 Inf -7.834 <.0001
## NA NA NA NA
## 0.3622 Inf 8.306 <.0001
## 0.3622 Inf 8.306 <.0001
## 0.2113 Inf 8.660 <.0001
## NA NA NA NA
## 0.2992 Inf 8.700 <.0001
## 0.1080 Inf 6.987 <.0001
## NA NA NA NA
## 0.1053 Inf 6.443 <.0001
## NA NA NA NA
## 0.2742 Inf 10.767 <.0001
## 0.0628 Inf -45.948 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.1925 Inf 11.374 <.0001
## 0.2742 Inf 10.767 <.0001
## 0.2054 Inf 11.357 <.0001
## 0.2054 Inf 11.357 <.0001
## NA NA NA NA
## 0.3221 Inf 10.210 <.0001
## 0.0816 Inf -2.989 0.4360
## NA NA NA NA
## 0.3588 Inf 9.787 <.0001
## 0.3588 Inf 9.787 <.0001
## 0.2054 Inf 11.357 <.0001
## NA NA NA NA
## 0.2951 Inf 10.527 <.0001
## NA NA NA NA
## 0.1237 Inf -0.618 1.0000
## NA NA NA NA
## 0.2817 Inf 7.799 <.0001
## 0.0903 Inf -40.319 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.2031 Inf 7.064 <.0001
## 0.2817 Inf 7.799 <.0001
## 0.2154 Inf 7.327 <.0001
## 0.2154 Inf 7.327 <.0001
## NA NA NA NA
## 0.3285 Inf 7.712 <.0001
## 0.1042 Inf -9.582 <.0001
## NA NA NA NA
## 0.3646 Inf 7.561 <.0001
## 0.3646 Inf 7.561 <.0001
## 0.2154 Inf 7.327 <.0001
## NA NA NA NA
## 0.3021 Inf 7.783 <.0001
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.2807 Inf 8.100 <.0001
## 0.0870 Inf -40.968 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.2017 Inf 7.493 <.0001
## 0.2807 Inf 8.100 <.0001
## 0.2140 Inf 7.730 <.0001
## 0.2140 Inf 7.730 <.0001
## NA NA NA NA
## 0.3276 Inf 7.966 <.0001
## 0.1014 Inf -9.096 <.0001
## NA NA NA NA
## 0.3638 Inf 7.788 <.0001
## 0.3638 Inf 7.788 <.0001
## 0.2140 Inf 7.730 <.0001
## NA NA NA NA
## 0.3011 Inf 8.062 <.0001
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.2677 Inf -21.805 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.3237 Inf -2.355 0.8988
## 0.3780 Inf 0.000 1.0000
## 0.3315 Inf -1.867 0.9955
## 0.3315 Inf -1.867 0.9955
## NA NA NA NA
## 0.4140 Inf 0.813 1.0000
## 0.2727 Inf -11.720 <.0001
## NA NA NA NA
## 0.4432 Inf 1.263 1.0000
## 0.4432 Inf 1.263 1.0000
## 0.3315 Inf -1.867 0.9955
## NA NA NA NA
## 0.3934 Inf 0.392 1.0000
## NA NA NA NA
## NA NA NA NA
## 0.1831 Inf 27.704 <.0001
## 0.2677 Inf 21.805 <.0001
## 0.1966 Inf 26.530 <.0001
## 0.1966 Inf 26.530 <.0001
## NA NA NA NA
## 0.3166 Inf 19.499 <.0001
## 0.0560 Inf 47.174 <.0001
## NA NA NA NA
## 0.3538 Inf 18.075 <.0001
## 0.3538 Inf 18.075 <.0001
## 0.1966 Inf 26.530 <.0001
## NA NA NA NA
## 0.2890 Inf 20.725 <.0001
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.3237 Inf 2.355 0.8988
## 0.2679 Inf 0.534 1.0000
## 0.2679 Inf 0.534 1.0000
## NA NA NA NA
## 0.3651 Inf 3.009 0.4201
## 0.1904 Inf -12.781 <.0001
## NA NA NA NA
## 0.3979 Inf 3.322 0.2085
## 0.3979 Inf 3.322 0.2085
## 0.2679 Inf 0.534 1.0000
## NA NA NA NA
## 0.3416 Inf 2.683 0.6900
## 0.3315 Inf -1.867 0.9955
## 0.3315 Inf -1.867 0.9955
## NA NA NA NA
## 0.4140 Inf 0.813 1.0000
## 0.2727 Inf -11.720 <.0001
## NA NA NA NA
## 0.4432 Inf 1.263 1.0000
## 0.4432 Inf 1.263 1.0000
## 0.3315 Inf -1.867 0.9955
## NA NA NA NA
## 0.3934 Inf 0.392 1.0000
## 0.2774 Inf 0.000 1.0000
## NA NA NA NA
## 0.3721 Inf 2.568 0.7761
## 0.2034 Inf -12.666 <.0001
## NA NA NA NA
## 0.4043 Inf 2.915 0.4964
## 0.4043 Inf 2.915 0.4964
## 0.2774 Inf 0.000 1.0000
## NA NA NA NA
## 0.3490 Inf 2.216 0.9487
## NA NA NA NA
## 0.3721 Inf 2.568 0.7761
## 0.2034 Inf -12.666 <.0001
## NA NA NA NA
## 0.4043 Inf 2.915 0.4964
## 0.4043 Inf 2.915 0.4964
## 0.2774 Inf 0.000 1.0000
## NA NA NA NA
## 0.3490 Inf 2.216 0.9487
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.3208 Inf -11.010 <.0001
## NA NA NA NA
## 0.4743 Inf 0.470 1.0000
## 0.4743 Inf 0.470 1.0000
## 0.3721 Inf -2.568 0.7761
## NA NA NA NA
## 0.4282 Inf -0.426 1.0000
## NA NA NA NA
## 0.3577 Inf 10.500 <.0001
## 0.3577 Inf 10.500 <.0001
## 0.2034 Inf 12.666 <.0001
## NA NA NA NA
## 0.2937 Inf 11.406 <.0001
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.5000 Inf 0.000 1.0000
## 0.4043 Inf -2.915 0.4964
## NA NA NA NA
## 0.4564 Inf -0.888 1.0000
## 0.4043 Inf -2.915 0.4964
## NA NA NA NA
## 0.4564 Inf -0.888 1.0000
## NA NA NA NA
## 0.3490 Inf 2.216 0.9487
## NA NA NA NA
##
## Results are averaged over the levels of: microsite
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 33 estimatesanimals_density_final <- photo_final %>% group_by(site_code,microsite,plot, shrub_density, common_name) %>% summarise(captures = sum(animal.hit))## `summarise()` has grouped output by 'site_code', 'microsite', 'plot',
## 'shrub_density'. You can override using the `.groups` argument.animals_density_final <- animals_density_final %>% filter(common_name != "Blank") %>% filter(common_name != "Mammal")pca_data_final <- animals_density_final ### Created new df for pca data
pca_data_final <- pca_data_final %>%
spread(common_name, captures) %>%
ungroup() %>%
dplyr::select(-site_code, -microsite, -plot) %>%
replace(is.na(.),0)
dim(pca_data_final)## [1] 24 35env_final <- read.csv("environment_final.csv") ### Drop Tecopa open 1, Tecopa open 4, since they have no animal observations.
dim(env_final)## [1] 24 5model010 <- adonis(pca_data_final ~ microsite*shrub_density, data = env_final)## 'adonis' will be deprecated: use 'adonis2' insteadmodel010## $aov.tab
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## microsite 1 0.2680 0.26799 1.0165 0.04244 0.376
## shrub_density 1 0.5102 0.51024 1.9354 0.08081 0.071 .
## Residuals 21 5.5363 0.26363 0.87675
## Total 23 6.3145 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $call
## adonis(formula = pca_data_final ~ microsite * shrub_density,
## data = env_final)
##
## $coefficients
## shrub_density American Robin Black-tailed Jackrabbit
## (Intercept) -10.557692 4.166667e-02 -11.346154
## microsite1 -14.057692 -4.166667e-02 -22.012821
## shrub_density 2.876923 9.476236e-19 4.661538
## microsite1:shrub_density NA NA NA
## Black-throated Sparrow Blunt-nosed Leopard Lizard
## (Intercept) 4.166667e-02 1.1185897
## microsite1 -4.166667e-02 1.0352564
## shrub_density 4.564605e-18 -0.1692308
## microsite1:shrub_density NA NA
## Bobcat Brewer's Blackbird
## (Intercept) -0.7019231 -2.4070513
## microsite1 -1.1185897 -2.8237179
## shrub_density 0.1692308 0.5538462
## microsite1:shrub_density NA NA
## California Ground Squirrel California Pocket Mouse
## (Intercept) 19.618590 0.33974359
## microsite1 13.868590 0.17307692
## shrub_density -1.769231 -0.03076923
## microsite1:shrub_density NA NA
## California Quail California Thrasher Common Raven
## (Intercept) -6.185897 -1.0 2.5961538
## microsite1 -7.352564 -1.0 -1.9038462
## shrub_density 1.292308 0.2 0.1384615
## microsite1:shrub_density NA NA NA
## Coyote Desert Cottontail Desert Iguana
## (Intercept) 3.2467949 -27.230769 1.958333e+00
## microsite1 -2.8365385 -28.897436 -1.958333e+00
## shrub_density 0.4153846 5.307692 3.783904e-16
## microsite1:shrub_density NA NA NA
## Giant Kangaroo Rat Great White Egret
## (Intercept) 6.8141026 4.166667e-02
## microsite1 4.6474359 -4.166667e-02
## shrub_density -0.7076923 4.564605e-18
## microsite1:shrub_density NA NA
## Greater Roadrunner Heermann's Kangaroo Rat Horned Lark
## (Intercept) -4.3814103 125.403846 0.38461538
## microsite1 -4.4647436 50.903846 0.38461538
## shrub_density 0.8307692 -4.538462 -0.04615385
## microsite1:shrub_density NA NA NA
## Killdeer Kit Fox Lark Sparrow
## (Intercept) 4.166667e-02 2.5288462 -1.8717949
## microsite1 -4.166667e-02 2.1121795 -2.2051282
## shrub_density 4.564605e-18 -0.3384615 0.3846154
## microsite1:shrub_density NA NA NA
## Loggerhead Shrike Merriam's Kangaroo Rat
## (Intercept) -1.1025641 1.06089744
## microsite1 -1.4358974 0.14423077
## shrub_density 0.2923077 -0.09230769
## microsite1:shrub_density NA NA
## Mohave Ground Squirrel Mourning Dove
## (Intercept) 2.916667e-01 -1.9551282
## microsite1 -2.916667e-01 -2.1217949
## shrub_density -1.570024e-16 0.3846154
## microsite1:shrub_density NA NA
## Nelson's Antelope Squirrel No CV Result
## (Intercept) 18.894231 -0.8685897
## microsite1 13.977564 -0.9519231
## shrub_density -2.092308 0.1692308
## microsite1:shrub_density NA NA
## Red-tailed Hawk Salinas Pocket Mouse Say's Phoebe
## (Intercept) -0.5641026 -0.35256410 0.253205128
## microsite1 -0.5641026 -0.51923077 0.003205128
## shrub_density 0.1076923 0.09230769 -0.015384615
## microsite1:shrub_density NA NA NA
## Vesper Sparrow Western whiptail
## (Intercept) 1.5801282 0.12820513
## microsite1 1.4967949 0.12820513
## shrub_density -0.1846154 -0.01538462
## microsite1:shrub_density NA NA
## White-tailed Antelope Squirrel
## (Intercept) 0.33653846
## microsite1 -0.08012821
## shrub_density -0.01538462
## microsite1:shrub_density NA
##
## $coef.sites
## 1 2 3 4
## (Intercept) 0.78080317 0.82434750 0.98306093 0.76605527
## microsite1 0.17504154 0.26134577 0.28849450 0.24337903
## shrub_density -0.03080745 -0.04609079 -0.04138812 -0.03373393
## microsite1:shrub_density NA NA NA NA
## 5 6 7 8
## (Intercept) 0.84838652 0.83124969 0.86997845 0.76272593
## microsite1 0.30313950 0.28542834 0.29904628 0.20499025
## shrub_density -0.05068255 -0.04597727 -0.04282439 -0.02976002
## microsite1:shrub_density NA NA NA NA
## 9 10 11 12
## (Intercept) 1.7481015 1.5203179 1.00167997 1.0125329
## microsite1 1.0946409 0.9430041 0.41975528 0.4948288
## shrub_density -0.1923781 -0.1650103 -0.07142752 -0.0794049
## microsite1:shrub_density NA NA NA NA
## 13 14 15 16
## (Intercept) 0.88500919 0.84806327 0.8076157 0.83755826
## microsite1 0.18017241 0.07715532 0.2071152 0.27863228
## shrub_density -0.03799694 -0.02089895 -0.0336203 -0.04242666
## microsite1:shrub_density NA NA NA NA
## 17 18 19 20
## (Intercept) 0.04881892 0.31671402 0.25149293 0.07189331
## microsite1 -0.82473427 -0.56994858 -0.63539881 -0.82608604
## shrub_density 0.13158888 0.08613734 0.09735222 0.13310018
## microsite1:shrub_density NA NA NA NA
## 21 22 23 24
## (Intercept) 1.12355908 1.11712532 1.06379973 0.830255162
## microsite1 0.41343026 0.46257310 0.31720419 0.079719825
## shrub_density -0.06585209 -0.07418334 -0.04509589 -0.006831989
## microsite1:shrub_density NA NA NA NA
##
## $f.perms
## [,1] [,2]
## [1,] 0.42567613 1.6145440
## [2,] 0.55164061 1.0902870
## [3,] 2.11448261 1.7774341
## [4,] 1.36365233 1.2634207
## [5,] 0.77722767 0.3427893
## [6,] 0.82030023 0.9880416
## [7,] 0.38188850 0.5671524
## [8,] 0.33887034 0.9687802
## [9,] 0.41865359 0.1675109
## [10,] 1.21971451 1.1376971
## [11,] 0.55696033 0.6344934
## [12,] 0.77840782 0.2585865
## [13,] 0.39993694 1.6319336
## [14,] 0.38387600 0.5104751
## [15,] 0.70338421 0.5375679
## [16,] 0.98799248 2.6060663
## [17,] 0.68667064 0.6208726
## [18,] 0.87453381 1.3576922
## [19,] 0.67518963 0.6541362
## [20,] 0.77324853 0.2665269
## [21,] 0.77704580 1.0394374
## [22,] 0.73599659 1.3133106
## [23,] 0.23274360 0.4443959
## [24,] 1.30336002 1.8999963
## [25,] 1.16014174 0.4038810
## [26,] 1.61473993 0.6060563
## [27,] 1.58917631 0.8940414
## [28,] 0.34141369 0.5217915
## [29,] 0.27440040 1.2435496
## [30,] 1.36481807 0.5366875
## [31,] 1.76876216 0.6281006
## [32,] 0.26026514 0.9878970
## [33,] 2.70423838 1.0036309
## [34,] 1.85116883 0.4887258
## [35,] 1.64174717 0.8959147
## [36,] 1.41627217 1.2222351
## [37,] 1.06043960 3.3711944
## [38,] 3.19918238 0.9379562
## [39,] 0.62223733 1.3685439
## [40,] 1.00443067 1.2619194
## [41,] 0.29222755 0.1809151
## [42,] 0.49577695 1.1309120
## [43,] 0.63298788 0.5052355
## [44,] 0.20842058 1.5482573
## [45,] 1.02952960 0.2508530
## [46,] 0.76120568 0.8428552
## [47,] 0.97881139 2.8360015
## [48,] 0.59632990 1.5377674
## [49,] 0.73912879 0.7019683
## [50,] 3.62777830 2.1098562
## [51,] 0.67789490 1.0268624
## [52,] 1.56635276 0.8085958
## [53,] 1.07717630 1.4393057
## [54,] 0.57284904 0.8766896
## [55,] 1.47364908 0.5751834
## [56,] 1.99679830 1.3605279
## [57,] 0.46284768 0.5414056
## [58,] 1.80964366 1.0383247
## [59,] 0.43285539 0.6481075
## [60,] 0.90260154 0.5838898
## [61,] 0.81583441 0.8773683
## [62,] 2.66610020 0.4439930
## [63,] 1.52262596 0.3348183
## [64,] 0.95908497 0.4279535
## [65,] 0.83322189 0.9099560
## [66,] 2.17042539 0.4409632
## [67,] 0.98106601 0.2831945
## [68,] 1.32170021 0.8207783
## [69,] 1.35296578 0.3469620
## [70,] 0.46540688 0.8476617
## [71,] 0.92988068 0.6207447
## [72,] 0.66447961 1.4579331
## [73,] 0.59033775 1.5648165
## [74,] 1.56707346 1.8551850
## [75,] 1.08302663 0.8570190
## [76,] 1.28780857 0.5925282
## [77,] 0.47891287 1.4337722
## [78,] 0.72002337 1.5214506
## [79,] 0.82007662 1.1019993
## [80,] 1.25132477 5.0408121
## [81,] 0.57207088 0.2251390
## [82,] 0.75058461 0.8654571
## [83,] 1.06139808 1.2438681
## [84,] 2.03029206 1.4827397
## [85,] 1.20743015 1.1488845
## [86,] 0.94166585 1.5039991
## [87,] 0.34567549 0.4143180
## [88,] 3.32316860 1.4847499
## [89,] 0.97011360 1.1405347
## [90,] 1.48016184 1.2849006
## [91,] 0.53846658 1.8824596
## [92,] 0.87245001 0.4209524
## [93,] 2.75490749 0.4885455
## [94,] 0.47310850 0.9284034
## [95,] 1.66909260 1.0563591
## [96,] 0.98590247 1.3061993
## [97,] 0.61341665 0.5225113
## [98,] 0.43471984 1.0161509
## [99,] 0.78170409 0.5183545
## [100,] 1.17846175 1.4244480
## [101,] 3.14040139 1.2148783
## [102,] 0.82253364 0.9332218
## [103,] 1.03786706 1.2280901
## [104,] 0.96841375 1.0440487
## [105,] 1.02109055 0.8431252
## [106,] 1.78561075 0.5405544
## [107,] 0.43958912 0.5622185
## [108,] 1.45605764 0.7206247
## [109,] 0.38607550 1.3717749
## [110,] 0.85132025 0.8787991
## [111,] 0.99777197 1.1206987
## [112,] 0.49657394 1.8428639
## [113,] 2.77406004 1.2672374
## [114,] 1.84414098 0.9328561
## [115,] 0.32837891 0.7242991
## [116,] 0.60293865 1.0657253
## [117,] 3.10031847 1.1562892
## [118,] 0.66070386 1.6633590
## [119,] 0.39632827 1.2714468
## [120,] 0.79697883 0.6175843
## [121,] 3.04106898 1.3577498
## [122,] 2.42038060 1.6231844
## [123,] 0.45759492 0.4011322
## [124,] 1.51959417 0.7126546
## [125,] 0.48602956 0.4905166
## [126,] 0.58136489 1.8686203
## [127,] 1.03320747 0.4874074
## [128,] 1.01303830 0.5042066
## [129,] 0.33139955 0.5710786
## [130,] 0.57022749 0.2781573
## [131,] 0.46715634 1.0847039
## [132,] 1.00581247 0.8984551
## [133,] 0.31740570 0.2978362
## [134,] 0.50637605 0.9242483
## [135,] 4.91207832 0.5961972
## [136,] 0.37728923 0.1759206
## [137,] 0.23045599 0.9242226
## [138,] 1.19776375 1.0393836
## [139,] 0.47885280 1.4791075
## [140,] 0.38346947 0.9248988
## [141,] 1.17265242 0.8799809
## [142,] 0.37691695 0.6131272
## [143,] 0.39583478 0.4792491
## [144,] 1.50591210 0.5281395
## [145,] 1.99846747 1.3059075
## [146,] 1.13521051 2.5073410
## [147,] 1.46813929 0.8832800
## [148,] 0.45426947 1.0309341
## [149,] 0.62955311 0.6645688
## [150,] 0.54952434 0.3568960
## [151,] 0.65907984 1.0565734
## [152,] 0.78490306 0.3501668
## [153,] 1.40852470 4.8610226
## [154,] 0.94346322 1.3021137
## [155,] 0.35697777 0.7316048
## [156,] 0.57503568 1.5090663
## [157,] 2.61281366 0.2097528
## [158,] 0.96943895 1.6850993
## [159,] 0.47376456 0.4818232
## [160,] 1.96506404 0.3202931
## [161,] 0.33223973 0.6106234
## [162,] 0.58890276 0.8652950
## [163,] 0.56662228 0.2961035
## [164,] 0.76447376 0.6709196
## [165,] 1.54581751 0.9364870
## [166,] 0.36154883 1.6331480
## [167,] 0.95838167 0.5411279
## [168,] 0.69875833 0.4383799
## [169,] 4.43386721 1.5061332
## [170,] 0.89413761 0.6801373
## [171,] 0.85342761 1.2601183
## [172,] 0.42038837 1.9451041
## [173,] 0.90130159 0.8984389
## [174,] 0.50168692 1.3888042
## [175,] 0.37385337 0.2167949
## [176,] 0.85037544 0.6323192
## [177,] 0.91865711 0.6016669
## [178,] 0.48835099 0.5529856
## [179,] 0.66182963 1.2227483
## [180,] 2.30062848 1.4615372
## [181,] 0.52278928 0.2790908
## [182,] 2.28317757 0.4141570
## [183,] 0.65446853 0.6759268
## [184,] 0.38020715 0.5792685
## [185,] 0.64095295 0.6499631
## [186,] 0.49271743 1.8565342
## [187,] 0.77910785 0.8731988
## [188,] 0.83747146 0.7693815
## [189,] 1.12558408 1.3504568
## [190,] 0.75855514 0.4936042
## [191,] 0.58779736 1.5444944
## [192,] 0.26087108 0.3362713
## [193,] 0.31514572 1.5809000
## [194,] 0.51316733 0.9176857
## [195,] 0.85635080 1.2309059
## [196,] 0.86452046 1.0329135
## [197,] 0.24518612 0.9236570
## [198,] 0.38742276 0.4362730
## [199,] 0.48637309 0.4676615
## [200,] 1.61585913 0.2446028
## [201,] 0.98249107 1.1390330
## [202,] 2.42523426 0.9298609
## [203,] 6.14555228 0.8498162
## [204,] 0.81496314 0.8541386
## [205,] 2.48098377 0.6251541
## [206,] 0.73432220 0.8373400
## [207,] 2.49613298 1.1939473
## [208,] 1.55586763 1.9311412
## [209,] 0.42389535 0.4593381
## [210,] 1.57096924 0.5900488
## [211,] 0.48581526 0.5820434
## [212,] 0.45891252 1.0332163
## [213,] 0.53070079 0.6347142
## [214,] 1.03285791 0.4060392
## [215,] 0.59285005 1.4950715
## [216,] 0.63766641 1.3210361
## [217,] 1.38490913 0.7675485
## [218,] 0.85329229 0.6547383
## [219,] 0.78388539 2.9530717
## [220,] 0.57723557 2.5224832
## [221,] 1.38384370 0.4099661
## [222,] 0.72026772 1.1778436
## [223,] 2.58495614 1.8628009
## [224,] 0.75769426 0.1209135
## [225,] 2.88004082 0.4950732
## [226,] 0.62381370 1.1619368
## [227,] 1.27591113 0.7743919
## [228,] 0.77436578 0.5540036
## [229,] 0.64836843 0.4348550
## [230,] 2.86708410 0.4721096
## [231,] 0.72771994 0.5395675
## [232,] 1.21715326 0.5633894
## [233,] 0.64522328 1.6523152
## [234,] 2.40036440 0.5629057
## [235,] 1.31450784 1.2980511
## [236,] 0.99530865 2.1033760
## [237,] 0.46894461 1.6877363
## [238,] 1.02083797 0.3706337
## [239,] 0.72608744 1.8055990
## [240,] 0.55130069 2.6027740
## [241,] 1.21558632 1.2862675
## [242,] 1.06092660 1.2854223
## [243,] 0.65175482 0.3126665
## [244,] 1.87085335 1.2987005
## [245,] 1.45845717 1.0893296
## [246,] 0.82850901 1.0900821
## [247,] 1.69807666 0.5855777
## [248,] 0.50115846 0.5860419
## [249,] 0.37988512 0.2564932
## [250,] 0.24827870 0.6375117
## [251,] 0.71041217 0.6575757
## [252,] 2.77467809 1.9539217
## [253,] 0.31093790 0.4382178
## [254,] 0.21981510 0.4436431
## [255,] 0.68095528 0.1798809
## [256,] 0.28334210 0.4197240
## [257,] 2.70308919 0.6616115
## [258,] 1.35689768 0.5473353
## [259,] 0.33592741 0.3913915
## [260,] 0.95296353 0.2662496
## [261,] 0.34246731 0.6403873
## [262,] 0.73357220 0.8697231
## [263,] 0.17160922 1.0401431
## [264,] 0.48430334 0.6859146
## [265,] 2.94078338 1.2480282
## [266,] 1.11909650 0.7018188
## [267,] 2.19129654 0.5895686
## [268,] 2.02056649 2.3494280
## [269,] 0.28285179 1.8155906
## [270,] 1.39478023 0.5453081
## [271,] 0.66537993 1.6626929
## [272,] 0.97042478 0.4391939
## [273,] 0.86535096 1.7136298
## [274,] 0.49524499 0.5203986
## [275,] 0.55434563 0.6634912
## [276,] 0.86214308 0.5487730
## [277,] 0.38749681 1.5833841
## [278,] 1.16782531 1.1134716
## [279,] 0.19641024 0.6250474
## [280,] 1.91729407 0.3817745
## [281,] 0.29835753 0.5453640
## [282,] 0.19356597 0.9795233
## [283,] 0.97585643 1.7018023
## [284,] 0.74421578 0.7787440
## [285,] 0.37472273 1.1295818
## [286,] 0.71644780 0.4892887
## [287,] 0.64277807 1.1326538
## [288,] 2.12578578 0.7277810
## [289,] 1.37313252 1.1720864
## [290,] 1.15063406 0.8939661
## [291,] 1.02383412 0.4539131
## [292,] 0.55427452 1.0089597
## [293,] 1.06267641 0.5274970
## [294,] 2.33146392 0.6528011
## [295,] 0.79759196 1.5945250
## [296,] 1.58690003 0.8760190
## [297,] 1.62341501 0.3643366
## [298,] 0.99474886 0.5338110
## [299,] 0.75525075 0.6704798
## [300,] 0.75788697 1.1325086
## [301,] 0.20132040 1.2756771
## [302,] 0.79772917 0.8896438
## [303,] 0.43816627 0.5990041
## [304,] 2.33702015 1.7893660
## [305,] 0.53310673 0.7122714
## [306,] 1.18597154 1.0980815
## [307,] 1.52876800 1.2609650
## [308,] 2.42006241 0.5929098
## [309,] 0.87414305 1.4138840
## [310,] 1.44693686 0.8225464
## [311,] 1.69099505 1.0916780
## [312,] 0.98297953 0.9864451
## [313,] 0.97115811 1.5233027
## [314,] 0.95008672 2.0601518
## [315,] 0.57176457 0.4626620
## [316,] 1.50694169 1.4665090
## [317,] 0.83971403 0.5182919
## [318,] 1.17850312 0.7702043
## [319,] 0.70458528 0.1972978
## [320,] 1.53767175 1.0355808
## [321,] 0.34640571 0.2403992
## [322,] 1.52101337 1.3992921
## [323,] 0.41328799 0.6386274
## [324,] 0.57195263 0.9262304
## [325,] 0.63532867 0.9801472
## [326,] 1.86592464 1.1233901
## [327,] 0.38663095 0.5293192
## [328,] 0.62644216 1.0785526
## [329,] 0.92300261 0.2881430
## [330,] 1.44096734 0.6412075
## [331,] 1.52389456 0.3988548
## [332,] 0.65071782 1.1126451
## [333,] 1.63227796 1.2864149
## [334,] 2.15250562 1.5306669
## [335,] 0.38433051 1.4704882
## [336,] 1.02811239 0.8212230
## [337,] 1.27407331 1.2745042
## [338,] 0.46807397 1.0520989
## [339,] 0.42216101 0.4120066
## [340,] 0.31510651 1.5409714
## [341,] 2.11580481 0.4152330
## [342,] 0.58521962 1.0244526
## [343,] 0.71259032 1.8867516
## [344,] 2.08214643 1.0787021
## [345,] 0.68124158 0.5080444
## [346,] 0.40314043 1.0784835
## [347,] 1.14306532 1.0255372
## [348,] 0.98723259 3.5493132
## [349,] 0.61062486 1.2952428
## [350,] 0.64803167 0.7710890
## [351,] 0.74357466 0.3642529
## [352,] 0.96545816 1.0849034
## [353,] 1.51338067 0.3473848
## [354,] 0.82846096 0.7471863
## [355,] 1.29805256 1.2700644
## [356,] 1.67752464 1.4977995
## [357,] 0.75738322 0.7143068
## [358,] 0.67360752 0.9446915
## [359,] 1.19078040 0.6775727
## [360,] 0.86535842 0.9080627
## [361,] 0.79825674 0.4006153
## [362,] 1.14471066 0.4389606
## [363,] 0.36291405 1.5213920
## [364,] 0.56181788 0.4813625
## [365,] 0.56990115 0.7583227
## [366,] 0.43723564 1.4059279
## [367,] 0.82916737 0.6126105
## [368,] 1.28242352 1.3170150
## [369,] 0.97997834 0.7493162
## [370,] 1.16696249 5.6589699
## [371,] 0.76921378 0.8166533
## [372,] 0.70468829 1.1900565
## [373,] 1.79138792 2.0128180
## [374,] 0.64453912 1.0610845
## [375,] 0.92357996 0.9982071
## [376,] 0.85222249 1.1902173
## [377,] 0.38075899 2.0068498
## [378,] 0.83342019 0.8173466
## [379,] 1.19059721 0.6802276
## [380,] 0.34480288 1.3710665
## [381,] 0.37022281 0.8947034
## [382,] 1.12551609 0.8534363
## [383,] 0.71460756 4.0283747
## [384,] 1.98330514 0.7579659
## [385,] 1.28700521 0.2758770
## [386,] 0.96022040 0.6752242
## [387,] 0.62220452 1.2409625
## [388,] 1.33141063 1.0956251
## [389,] 0.83737378 1.0342799
## [390,] 0.72151935 0.5969956
## [391,] 0.72975937 0.6994515
## [392,] 0.99597876 1.1109323
## [393,] 1.10022626 0.6698022
## [394,] 0.69207654 1.0170631
## [395,] 1.35888650 0.4006011
## [396,] 0.75301574 0.7120450
## [397,] 0.49158371 1.1175651
## [398,] 1.00195688 0.1015228
## [399,] 2.29629964 1.3777328
## [400,] 0.32272010 0.7290804
## [401,] 0.17953096 1.9704037
## [402,] 0.38904868 1.1793929
## [403,] 0.81648157 0.8757606
## [404,] 1.73168768 0.3897616
## [405,] 1.48266593 0.8704389
## [406,] 0.80930778 2.0699907
## [407,] 1.70712347 0.3857210
## [408,] 1.79691891 2.8166007
## [409,] 0.65831584 1.0426219
## [410,] 0.62845267 0.1662529
## [411,] 1.04780847 0.7397657
## [412,] 0.46000038 0.5390988
## [413,] 0.55222585 0.4916957
## [414,] 0.53078523 1.2708212
## [415,] 0.08089913 0.6944434
## [416,] 3.27694771 0.5489731
## [417,] 0.81866053 1.5146732
## [418,] 0.72366259 1.0228785
## [419,] 1.05278702 0.5721290
## [420,] 0.53843199 0.7268367
## [421,] 0.65628768 0.3980721
## [422,] 1.12168545 0.8888951
## [423,] 1.51289609 1.1499097
## [424,] 0.83035598 1.3534165
## [425,] 0.60340296 1.8219189
## [426,] 0.81102136 1.4958243
## [427,] 0.66784452 0.9169890
## [428,] 1.22490347 2.1789131
## [429,] 0.27808220 1.2220281
## [430,] 0.81306573 0.6669181
## [431,] 0.33490273 0.4533955
## [432,] 0.95343868 0.7793427
## [433,] 0.62796349 1.5548269
## [434,] 1.07802876 1.3347549
## [435,] 0.50807284 0.3943221
## [436,] 1.04976562 0.4165601
## [437,] 0.47352332 0.5450595
## [438,] 0.42097157 2.1847767
## [439,] 0.99237602 2.0487011
## [440,] 2.06166992 0.5603587
## [441,] 2.46713606 0.7628723
## [442,] 1.23458204 1.2897111
## [443,] 0.78259567 0.3384434
## [444,] 1.68714295 1.4217455
## [445,] 1.00174589 0.7956361
## [446,] 0.45437809 1.5111017
## [447,] 0.54930544 0.6470883
## [448,] 1.11404431 0.5382032
## [449,] 1.96563511 0.2106650
## [450,] 0.84921752 2.2568376
## [451,] 0.81366904 1.6260555
## [452,] 0.35400419 3.0595643
## [453,] 0.54803405 1.0607976
## [454,] 0.86086667 1.0482858
## [455,] 0.59174002 1.1756202
## [456,] 1.71336166 1.0245616
## [457,] 3.47291037 1.5119148
## [458,] 0.72213639 0.7497430
## [459,] 0.63911633 0.9097839
## [460,] 0.96623317 2.8812669
## [461,] 1.27577835 1.1851489
## [462,] 0.72136566 0.7898367
## [463,] 0.54700216 0.3417409
## [464,] 1.61860788 0.2179308
## [465,] 0.59815763 1.2942483
## [466,] 0.33399623 0.3183231
## [467,] 2.59832776 1.3691505
## [468,] 1.65980148 0.4460687
## [469,] 1.69302941 0.4433658
## [470,] 0.78030654 0.6544134
## [471,] 0.23955609 0.5337977
## [472,] 2.09202833 1.8130130
## [473,] 1.72310758 2.4553426
## [474,] 0.47112435 0.1840907
## [475,] 0.76275168 0.3959936
## [476,] 0.57666656 0.9160500
## [477,] 0.32384817 0.5758912
## [478,] 0.43227066 0.6666277
## [479,] 1.21474194 0.3407410
## [480,] 2.04469302 0.9644066
## [481,] 1.39731017 0.9533926
## [482,] 0.45095513 0.6275487
## [483,] 0.22663088 1.4958555
## [484,] 1.59812347 0.6001751
## [485,] 0.73238608 1.3611388
## [486,] 2.03226541 2.5844695
## [487,] 0.82016054 0.6962277
## [488,] 0.97439421 0.5559541
## [489,] 1.01781015 1.7316152
## [490,] 2.10958922 1.0302925
## [491,] 1.60651943 0.3614439
## [492,] 0.99119957 0.9007120
## [493,] 0.99077276 0.7117627
## [494,] 3.30039295 0.8757868
## [495,] 1.21721154 0.7117148
## [496,] 1.75018606 0.5139769
## [497,] 1.12787909 0.9845356
## [498,] 0.14669185 0.9364738
## [499,] 1.71533073 0.1534456
## [500,] 3.69272492 0.2462970
## [501,] 1.08697995 3.0167614
## [502,] 0.55389229 0.8575570
## [503,] 1.34308433 0.2303450
## [504,] 0.58315922 0.8442700
## [505,] 1.09973233 2.5966845
## [506,] 1.11556930 0.2889067
## [507,] 0.66007020 1.0925881
## [508,] 0.38238326 0.5971220
## [509,] 1.15017786 1.3632907
## [510,] 0.83595597 0.9904297
## [511,] 0.42585403 0.8500826
## [512,] 0.60470785 0.6565290
## [513,] 0.74731283 1.7062220
## [514,] 0.68054212 1.6357412
## [515,] 1.41613818 1.2556475
## [516,] 1.52312787 1.9097085
## [517,] 0.56707080 0.2033227
## [518,] 1.37261207 0.9144067
## [519,] 0.97302203 0.4080392
## [520,] 1.31259185 1.0256909
## [521,] 0.58146991 1.0621807
## [522,] 0.76125397 0.5093119
## [523,] 0.68697169 0.2347925
## [524,] 1.78486712 0.1888509
## [525,] 1.05382574 0.2334618
## [526,] 1.08427093 0.6115727
## [527,] 1.05029012 0.4617349
## [528,] 1.42977697 0.2719962
## [529,] 1.87001109 0.6798916
## [530,] 0.73252199 1.2666246
## [531,] 1.37756729 0.6163239
## [532,] 1.36585125 0.7784825
## [533,] 0.31703897 2.1755728
## [534,] 0.62102457 0.4823674
## [535,] 0.65290608 1.4935561
## [536,] 2.77002166 2.3648075
## [537,] 1.08097884 0.4586641
## [538,] 1.09748577 0.9107791
## [539,] 0.48377379 0.9690688
## [540,] 0.57043396 0.5709835
## [541,] 1.85646047 1.9866537
## [542,] 0.54657993 0.4595772
## [543,] 0.50716002 2.2703757
## [544,] 1.39018212 0.7555393
## [545,] 0.87054530 0.6372790
## [546,] 0.66065587 0.5236003
## [547,] 1.29192708 0.4713085
## [548,] 1.04600301 0.7069883
## [549,] 0.81716267 0.6000037
## [550,] 0.17468902 0.6224047
## [551,] 0.59258379 0.7208226
## [552,] 0.89283619 0.6469507
## [553,] 1.24231434 1.0312131
## [554,] 0.88263319 2.2002747
## [555,] 1.26173443 0.7407020
## [556,] 0.61698722 1.2372282
## [557,] 0.87082505 2.9967348
## [558,] 0.69817054 2.1533453
## [559,] 1.09875550 0.8918105
## [560,] 1.09630818 0.2263179
## [561,] 1.13333734 1.0778343
## [562,] 1.19809646 0.7478371
## [563,] 3.18677298 0.6419978
## [564,] 0.52567611 0.5852555
## [565,] 2.92760609 0.9706206
## [566,] 1.38528301 1.7687685
## [567,] 0.63453709 1.5096771
## [568,] 0.44827980 0.6260115
## [569,] 2.53541760 0.4186630
## [570,] 0.43746119 0.5714581
## [571,] 1.95954664 0.6649248
## [572,] 1.87730294 1.0751614
## [573,] 2.20711532 1.2925296
## [574,] 1.43316903 0.8588184
## [575,] 0.81192677 1.2305056
## [576,] 0.94472861 0.8771534
## [577,] 0.42660585 0.5287869
## [578,] 0.62716418 1.4296896
## [579,] 0.93194116 0.5808628
## [580,] 2.00369086 0.7943952
## [581,] 1.01992809 1.0044794
## [582,] 0.49053490 0.9962780
## [583,] 0.72033174 0.5823973
## [584,] 0.71609681 0.5199788
## [585,] 2.76637242 1.0422581
## [586,] 0.43797151 0.8371441
## [587,] 0.70504001 1.7573503
## [588,] 0.69701489 1.7312972
## [589,] 1.91547699 1.0499265
## [590,] 1.43663602 0.5929155
## [591,] 0.35853381 0.7335821
## [592,] 0.87610947 1.1499394
## [593,] 0.98576201 0.8238989
## [594,] 0.51266254 0.7846399
## [595,] 1.84886348 1.8634414
## [596,] 0.12956769 0.9568176
## [597,] 0.45173343 0.9294444
## [598,] 0.62575399 1.3685713
## [599,] 0.54527364 0.7685571
## [600,] 0.43631019 0.4984692
## [601,] 1.80074463 0.3480476
## [602,] 0.71670712 2.6768234
## [603,] 0.78690373 0.5033236
## [604,] 1.09533740 0.8776433
## [605,] 0.61127390 1.2531063
## [606,] 1.25620187 1.5178498
## [607,] 1.73657118 0.4053408
## [608,] 1.17211270 0.5330481
## [609,] 0.67979871 2.0439311
## [610,] 3.91142465 0.7138051
## [611,] 1.07249261 0.7841150
## [612,] 1.01791795 1.2237104
## [613,] 1.92649569 0.9930600
## [614,] 1.03270846 0.7044815
## [615,] 0.29162684 2.1833821
## [616,] 2.16863451 0.2143884
## [617,] 1.96349068 1.4132107
## [618,] 0.81074695 0.5727137
## [619,] 0.79058724 0.8805230
## [620,] 0.33130476 0.6212667
## [621,] 2.27069021 1.1203673
## [622,] 0.40567475 0.3666869
## [623,] 0.45596696 0.9339854
## [624,] 0.88248195 0.5978097
## [625,] 1.25001002 2.0718046
## [626,] 1.26221845 3.0229567
## [627,] 0.53441091 2.2058313
## [628,] 0.39944445 0.7851544
## [629,] 2.45129576 0.2037824
## [630,] 0.43634419 0.6977097
## [631,] 0.70248138 0.6441429
## [632,] 1.19841839 0.8238524
## [633,] 0.42638481 1.1484546
## [634,] 1.24752097 0.8574953
## [635,] 0.68025214 0.4388239
## [636,] 0.30361330 0.9438826
## [637,] 0.65519324 1.7555445
## [638,] 0.51202536 2.1076696
## [639,] 1.09671517 0.3237157
## [640,] 0.06410089 0.6456168
## [641,] 1.28770211 0.9187439
## [642,] 0.45237349 1.0367428
## [643,] 1.51736784 1.2319641
## [644,] 1.47723830 1.5225434
## [645,] 0.74414493 0.7286364
## [646,] 0.54730097 1.2567242
## [647,] 0.50390316 1.4984220
## [648,] 0.59912692 1.3006924
## [649,] 0.91776438 3.1578943
## [650,] 1.13050017 2.4835308
## [651,] 1.36690891 0.8275713
## [652,] 0.28769551 0.4335568
## [653,] 1.45309772 1.1664955
## [654,] 0.48393192 0.1949628
## [655,] 2.32019470 1.8347398
## [656,] 2.91215789 2.4479782
## [657,] 0.64257869 1.1152602
## [658,] 0.61146883 0.8697240
## [659,] 0.54745071 0.8294598
## [660,] 0.45358316 0.9094684
## [661,] 1.86209223 1.0378279
## [662,] 0.31280313 0.6819495
## [663,] 0.99062940 0.5688196
## [664,] 0.58362834 1.4014366
## [665,] 1.29767475 0.8131148
## [666,] 0.56368661 0.3386420
## [667,] 1.06385174 1.1657908
## [668,] 1.80809713 0.3119592
## [669,] 0.34026775 0.1910332
## [670,] 0.44402039 1.5982165
## [671,] 1.63011924 1.8896159
## [672,] 1.12712054 2.8358096
## [673,] 0.68802672 1.7968997
## [674,] 0.84164999 1.2089139
## [675,] 0.17732828 0.6986172
## [676,] 0.81378405 0.3538056
## [677,] 0.24737165 0.8811370
## [678,] 1.24936104 0.2004780
## [679,] 1.64684394 0.4338029
## [680,] 0.37083646 0.4663276
## [681,] 0.87877279 0.6704607
## [682,] 0.84518757 0.4822439
## [683,] 0.60785272 1.8800410
## [684,] 1.15912474 1.5032783
## [685,] 1.14708780 0.9060396
## [686,] 2.43032897 1.8205028
## [687,] 0.62729858 0.5557188
## [688,] 1.17925745 0.2147091
## [689,] 0.43596949 0.4780095
## [690,] 1.33615549 0.7528482
## [691,] 1.00047034 1.3301800
## [692,] 0.65669788 0.5665422
## [693,] 0.47187980 0.3592098
## [694,] 3.00277785 0.4115450
## [695,] 1.28054376 1.3477040
## [696,] 0.23336416 0.6106067
## [697,] 0.87435723 0.5336155
## [698,] 0.86162839 0.7488722
## [699,] 1.68367652 1.2946814
## [700,] 0.17237899 1.0905128
## [701,] 0.74348733 0.5607427
## [702,] 1.40771273 1.0288495
## [703,] 0.79566112 0.4352144
## [704,] 0.92249555 0.4774840
## [705,] 1.08501238 1.5757038
## [706,] 0.75082305 3.5686703
## [707,] 0.57731478 0.4023920
## [708,] 0.94643188 0.5548729
## [709,] 1.33908230 1.4563204
## [710,] 1.11852426 0.9638878
## [711,] 1.51690307 2.2711093
## [712,] 0.96896613 1.0824847
## [713,] 1.90003341 0.3577276
## [714,] 1.34869392 1.3300138
## [715,] 1.75116271 2.1429531
## [716,] 1.10192045 0.7127888
## [717,] 1.92411804 1.1626389
## [718,] 0.95782382 1.3195086
## [719,] 0.46452066 1.0266345
## [720,] 2.33327670 0.7535329
## [721,] 0.70431961 0.4095754
## [722,] 0.61889482 0.5968625
## [723,] 2.54611203 1.1959639
## [724,] 0.69575971 0.5184066
## [725,] 0.55646625 0.7512479
## [726,] 0.63142744 1.2323042
## [727,] 1.31411681 0.4014105
## [728,] 1.46836050 3.7874602
## [729,] 3.87787566 0.2946527
## [730,] 2.07044077 0.2412865
## [731,] 0.31472390 0.8525800
## [732,] 1.19488888 3.6282645
## [733,] 0.77512006 1.0336877
## [734,] 1.54727412 1.1484797
## [735,] 0.76731719 4.8371090
## [736,] 0.90143834 0.8057870
## [737,] 0.60571514 0.5362546
## [738,] 0.66206677 0.9580251
## [739,] 1.25268583 0.7769230
## [740,] 0.69707197 0.4235283
## [741,] 1.90191685 0.2133243
## [742,] 0.75484078 1.5204905
## [743,] 0.86950891 0.6666057
## [744,] 1.25934808 0.9322101
## [745,] 2.20075944 1.5095102
## [746,] 1.61487831 0.4112771
## [747,] 0.72795970 0.7164128
## [748,] 2.16613421 0.9706847
## [749,] 0.42425513 0.9650048
## [750,] 0.82657824 0.5628484
## [751,] 1.25700522 0.8411519
## [752,] 0.58075094 0.7177401
## [753,] 1.56374970 0.8098214
## [754,] 0.12293582 1.6659026
## [755,] 0.72355632 0.6119912
## [756,] 0.64205408 0.5095687
## [757,] 1.91215703 0.6281910
## [758,] 2.36627290 1.1427033
## [759,] 0.29231523 1.0357593
## [760,] 1.93931725 1.1536789
## [761,] 1.34474488 1.1324691
## [762,] 0.40912285 0.2581767
## [763,] 0.58505554 0.6586129
## [764,] 0.53584804 0.2087720
## [765,] 0.49260728 0.3984549
## [766,] 0.86919870 0.5143073
## [767,] 0.61580808 0.5680969
## [768,] 2.15011608 0.6116784
## [769,] 0.33507967 0.5167507
## [770,] 0.48098977 0.4555907
## [771,] 0.29323257 0.3241618
## [772,] 0.43807658 0.5522117
## [773,] 0.59945977 1.2956945
## [774,] 0.64754053 0.8014917
## [775,] 4.30185599 0.3846354
## [776,] 0.43788330 1.0821829
## [777,] 0.22300111 0.6332761
## [778,] 0.22293963 0.8051476
## [779,] 0.58513127 0.8011112
## [780,] 1.59291443 0.5808460
## [781,] 0.58392704 1.1765356
## [782,] 0.17677251 0.8250374
## [783,] 0.56452957 2.1078476
## [784,] 0.30953936 1.1589478
## [785,] 0.99090843 1.1517757
## [786,] 0.98040959 2.0987666
## [787,] 0.23938546 0.9690597
## [788,] 0.77750479 0.8158301
## [789,] 0.37100569 1.0120035
## [790,] 3.01703720 0.7501027
## [791,] 1.06976902 1.7932978
## [792,] 1.30279937 0.7004976
## [793,] 1.59848449 0.7508667
## [794,] 0.80944427 0.2542158
## [795,] 0.88999117 0.9420539
## [796,] 0.52528486 0.5221366
## [797,] 0.50007397 0.3643280
## [798,] 0.82042810 0.6728878
## [799,] 0.78454906 1.1416047
## [800,] 0.58017452 0.8771815
## [801,] 0.58733497 1.9087884
## [802,] 0.62780148 0.8366287
## [803,] 0.53714647 0.4296790
## [804,] 1.06521888 1.0714702
## [805,] 1.85018006 1.0976054
## [806,] 0.47571805 1.0192515
## [807,] 0.84355869 0.2971449
## [808,] 0.83129419 1.2064371
## [809,] 2.18007347 1.1860421
## [810,] 0.62921261 1.7230174
## [811,] 0.89046462 1.1517023
## [812,] 0.79155056 0.7318432
## [813,] 2.97201729 1.2631694
## [814,] 0.40443798 2.2559094
## [815,] 2.16070158 1.0180115
## [816,] 2.20259456 1.1656339
## [817,] 0.98586925 0.6101497
## [818,] 1.01084350 1.0439240
## [819,] 0.99421000 0.8471173
## [820,] 1.42289198 1.5079117
## [821,] 1.77763822 0.2671525
## [822,] 0.72740517 0.9221009
## [823,] 1.34807090 0.8437444
## [824,] 1.97368030 0.4628382
## [825,] 1.87347928 0.1084476
## [826,] 0.77189029 0.6582549
## [827,] 0.51157048 0.8574418
## [828,] 0.61625527 0.4604354
## [829,] 0.33021038 1.2718445
## [830,] 1.05619982 0.9252157
## [831,] 0.41004443 0.2056754
## [832,] 0.43423724 0.3809702
## [833,] 0.34953049 0.9424481
## [834,] 0.52316591 1.0068131
## [835,] 0.48179467 0.3467477
## [836,] 0.72180482 0.9515771
## [837,] 0.51713112 0.6882767
## [838,] 1.04191204 0.5468752
## [839,] 1.72526810 0.6775245
## [840,] 1.35627011 0.1454602
## [841,] 0.98480166 0.9372470
## [842,] 1.07184983 0.3412724
## [843,] 0.71307595 1.4812772
## [844,] 1.73582392 1.0994799
## [845,] 0.81931911 0.5207496
## [846,] 0.68225064 1.1549170
## [847,] 2.39552530 0.3271115
## [848,] 1.14276671 0.5780169
## [849,] 0.15456494 0.4457840
## [850,] 0.72135799 0.8564635
## [851,] 1.27978110 0.9607251
## [852,] 0.75500830 0.7582362
## [853,] 1.12691139 0.4718323
## [854,] 0.66224421 1.3208272
## [855,] 1.15967096 0.8357068
## [856,] 1.29891517 1.1874824
## [857,] 0.70987749 0.6776459
## [858,] 0.72410982 0.7047933
## [859,] 0.59130087 1.1651493
## [860,] 0.79299956 0.4904724
## [861,] 0.43132159 0.4320980
## [862,] 0.21940303 0.9033722
## [863,] 3.64712935 1.3515488
## [864,] 1.20192997 0.2259046
## [865,] 0.82761195 1.5669602
## [866,] 0.64242190 0.3229068
## [867,] 1.04300250 1.0263216
## [868,] 0.76909200 0.7987564
## [869,] 0.47430078 0.8824073
## [870,] 1.20097867 0.9436169
## [871,] 0.75480514 0.8015523
## [872,] 2.07067423 0.5653110
## [873,] 1.01091984 1.0749982
## [874,] 0.74822830 0.2524207
## [875,] 0.50627477 1.4498846
## [876,] 3.67380311 0.3947072
## [877,] 0.61558279 0.4239835
## [878,] 2.02852571 0.4716817
## [879,] 3.00453875 0.5999413
## [880,] 0.47233078 0.9914937
## [881,] 1.19010198 1.8271512
## [882,] 0.84069588 0.5769016
## [883,] 1.21017329 1.9544810
## [884,] 1.45286238 2.6720118
## [885,] 0.50628462 0.4703280
## [886,] 3.33880529 0.6767213
## [887,] 0.42762127 0.7027358
## [888,] 0.54730047 1.0566285
## [889,] 1.28496402 1.6346632
## [890,] 2.59480104 0.5306793
## [891,] 0.87756229 3.0428468
## [892,] 1.91582978 0.5551037
## [893,] 0.40764128 1.1613811
## [894,] 0.46184048 0.4867443
## [895,] 0.51248911 0.5677282
## [896,] 0.44506752 0.4898592
## [897,] 2.84762507 2.1723468
## [898,] 0.66643576 0.6528615
## [899,] 0.62206285 0.3031422
## [900,] 1.21292638 0.5493861
## [901,] 0.99578030 0.6886083
## [902,] 0.76168383 0.4104400
## [903,] 1.93261775 0.4114208
## [904,] 0.69262381 0.4837668
## [905,] 0.43014873 0.8563557
## [906,] 0.66553174 0.6027835
## [907,] 1.41819254 0.3048096
## [908,] 0.79747221 1.5975816
## [909,] 1.83927012 0.8469353
## [910,] 0.66057521 0.7649361
## [911,] 0.81194740 0.6182865
## [912,] 0.67461520 1.1008965
## [913,] 1.88797929 0.9637572
## [914,] 0.77479496 0.8749445
## [915,] 0.68644519 0.9762334
## [916,] 0.86744229 0.8108681
## [917,] 0.93694062 0.6633495
## [918,] 0.63062012 1.2593403
## [919,] 1.26478804 2.0390032
## [920,] 1.25157351 0.5196868
## [921,] 0.35284311 0.4603766
## [922,] 0.78456296 0.4206922
## [923,] 0.32472507 0.4323342
## [924,] 0.25727667 1.6986068
## [925,] 1.70763546 2.0250919
## [926,] 0.67218346 0.9214438
## [927,] 0.70707210 0.5233351
## [928,] 0.50835619 0.5687120
## [929,] 0.86851381 0.8228832
## [930,] 2.10031865 0.6848575
## [931,] 0.26361145 0.6546123
## [932,] 0.98148208 0.2832739
## [933,] 2.39284956 1.4650066
## [934,] 0.04023172 1.0594909
## [935,] 0.95190734 1.9269836
## [936,] 0.45612927 0.7400490
## [937,] 0.39002183 0.4475721
## [938,] 0.70261876 1.6190117
## [939,] 0.77694349 1.1815229
## [940,] 0.92109829 0.5445483
## [941,] 0.84989611 0.7826478
## [942,] 0.77277748 0.6856594
## [943,] 0.62829219 0.7889788
## [944,] 0.43509812 0.9087642
## [945,] 1.14501891 1.5746326
## [946,] 0.76629638 0.6343912
## [947,] 0.93991113 0.1595323
## [948,] 0.93773796 1.6091783
## [949,] 0.87200184 1.1630009
## [950,] 1.33776264 1.1057329
## [951,] 0.73711583 0.8860939
## [952,] 0.44261755 0.9148329
## [953,] 0.71741567 1.3538343
## [954,] 0.21711328 0.4746137
## [955,] 0.82589914 1.4652563
## [956,] 0.61546484 0.7581856
## [957,] 2.55694374 0.9901904
## [958,] 0.48593468 1.6158228
## [959,] 0.29848584 1.1080153
## [960,] 0.39015486 0.9626104
## [961,] 0.29414862 0.5014723
## [962,] 0.55295930 1.2537841
## [963,] 1.76909985 0.8207305
## [964,] 0.70067870 2.2439625
## [965,] 2.97164954 0.6126692
## [966,] 0.34168400 0.8891289
## [967,] 0.56317001 0.7408331
## [968,] 2.15714643 1.1597120
## [969,] 0.65554662 0.8532540
## [970,] 1.29204481 0.2259971
## [971,] 0.48160159 0.6233436
## [972,] 1.57116442 1.5033480
## [973,] 1.02378447 0.7649779
## [974,] 0.44172995 2.7319639
## [975,] 0.69508738 0.8874048
## [976,] 0.53530281 1.0046028
## [977,] 0.86731373 0.8765445
## [978,] 0.55448186 0.3190288
## [979,] 0.91530582 0.4833298
## [980,] 0.43981035 0.8824495
## [981,] 1.34811785 2.8718457
## [982,] 3.73387358 0.6677432
## [983,] 0.83973527 0.9602228
## [984,] 1.82446389 0.2438962
## [985,] 1.20317198 0.6127901
## [986,] 1.28636216 0.1840904
## [987,] 0.31453472 0.7717767
## [988,] 1.26768606 1.0169165
## [989,] 2.29964320 0.6336563
## [990,] 0.92030976 1.9316230
## [991,] 1.11494021 0.6238103
## [992,] 0.77004246 1.0983543
## [993,] 1.39372409 0.7553803
## [994,] 0.67723414 0.8657639
## [995,] 0.34473828 0.9032336
## [996,] 0.81423396 2.2875280
## [997,] 0.49961996 1.1116194
## [998,] 0.85828440 1.4167456
## [999,] 0.57267189 0.3282864
##
## $model.matrix
## (Intercept) microsite1 shrub_density
## 1 1 1 11
## 2 1 1 12
## 3 1 -1 0
## 4 1 -1 0
## 5 1 1 11
## 6 1 1 10
## 7 1 -1 0
## 8 1 -1 0
## 9 1 1 14
## 10 1 1 13
## 11 1 -1 0
## 12 1 -1 0
## 13 1 1 11
## 14 1 1 11
## 15 1 -1 0
## 16 1 -1 0
## 17 1 1 10
## 18 1 1 11
## 19 1 1 11
## 20 1 1 10
## 21 1 -1 0
## 22 1 -1 0
## 23 1 -1 0
## 24 1 -1 0
##
## $terms
## pca_data_final ~ microsite * shrub_density
## attr(,"variables")
## list(pca_data_final, microsite, shrub_density)
## attr(,"factors")
## microsite shrub_density microsite:shrub_density
## pca_data_final 0 0 0
## microsite 1 0 1
## shrub_density 0 1 1
## attr(,"term.labels")
## [1] "microsite" "shrub_density"
## [3] "microsite:shrub_density"
## attr(,"order")
## [1] 1 1 2
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
##
## attr(,"class")
## [1] "adonis"dist_final <- vegdist(pca_data_final, species = "bray")
res_final <- pcoa(dist_final)
p02 <- as.data.frame(res_final$vectors)%>%
dplyr::select(Axis.1, Axis.2) %>%
bind_cols(env_final,.)
p02$microsite <- ifelse(p02$microsite == "Density", "Shrub", p02$microsite)
pcoa_final <- ggplot(p02, aes(Axis.1, Axis.2, group = microsite)) +
geom_point(aes(color = microsite)) +
geom_text(aes(label=plot), hjust = 0, vjust = 0, check_overlap = TRUE, nudge_x = 0.01)+
scale_color_brewer(palette = "Set1") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
labs(color = "Microsite")## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.pcoa_final <- pcoa_final + labs(x = "Shrub Density Gradient", y = "Community Composition") + scale_color_manual(
values = c("Shrub" = "#009900", "Open" = "#0066cc"), # Change color codes here
labels = c("Shrub" = "Shrub", "Open" = "Open")) + coord_fixed(ratio = 1)## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.pcoa_final
model020 <- betadisper(dist_final, env_final$microsite)
model020##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_final, group = env_final$microsite)
##
## No. of Positive Eigenvalues: 17
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## Density Open
## 0.5034 0.4680
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 23 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.6390 1.2441 0.9185 0.4221 0.3745 0.2048 0.1861 0.1553anova(model020)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 1 0.00750 0.0074952 0.2553 0.6184
## Residuals 22 0.64591 0.0293594permutest(model020,pairwise = TRUE, permutations = 99)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.00750 0.0074952 0.2553 99 0.68
## Residuals 22 0.64591 0.0293594
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Density Open
## Density 0.67
## Open 0.6184model020.HSD <- TukeyHSD(model020)
model020.HSD## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = distances ~ group, data = df)
##
## $group
## diff lwr upr p adj
## Open-Density -0.03534393 -0.1804147 0.1097268 0.6183989boxplot(model020)
model030 <- betadisper(dist_final, env_final$shrub_density)
model030##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_final, group = env_final$shrub_density)
##
## No. of Positive Eigenvalues: 17
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## 0 10 11 12 13 14
## 0.4680 0.3766 0.4847 0.0000 0.0000 0.0000
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 23 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.6390 1.2441 0.9185 0.4221 0.3745 0.2048 0.1861 0.1553anova(model030)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 5 0.58003 0.11601 2.233 0.09554 .
## Residuals 18 0.93511 0.05195
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1permutest(model030,pairwise = TRUE, permutations = 99)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 5 0.58003 0.11601 2.233 99 0.12
## Residuals 18 0.93511 0.05195
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## 0 10 11 12 13 14
## 0 0.54000 0.91000
## 10 0.55757 0.60000
## 11 0.86321 0.60937
## 12
## 13
## 14model030.HSD <- TukeyHSD(model030)
boxplot(model030)
### Shows a significant difference between community composition of tested sites (Carrizo -> Cuyama -> Tecopa)
model040 <- betadisper(dist_final, env_final$site)
model040##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_final, group = env_final$site)
##
## No. of Positive Eigenvalues: 17
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## Carrizo Cuyama Tecopa
## 0.2734 0.3349 0.4558
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 23 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.6390 1.2441 0.9185 0.4221 0.3745 0.2048 0.1861 0.1553anova(model040)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 2 0.13782 0.068911 3.7363 0.04091 *
## Residuals 21 0.38732 0.018444
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1permutest(model040,pairwise = TRUE, permutations = 99)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 2 0.13782 0.068911 3.7363 99 0.07 .
## Residuals 21 0.38732 0.018444
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Carrizo Cuyama Tecopa
## Carrizo 0.420000 0.05
## Cuyama 0.425689 0.07
## Tecopa 0.016550 0.067329model040.HSD <- TukeyHSD(model040)
supp_plot <- boxplot(model040, xlab = "Region")
### Temperature Data 2022
Temp_2022 <- read_csv("Temp Data 2022.csv")## Rows: 52443 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): researcher, site, site_code, microsite, time_block
## dbl (5): microsite_number, pendent_number, pendent_ID, rep, temp
## date (1): date
## time (1): time
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Temp_2022_final <- Temp_2022 %>%
group_by(site_code, microsite) %>%
summarise(mean_temp = mean(temp), max_temp = max(temp))## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.###Ground Temp Data
Ground_Temp_2022 <- read_csv("Gradient Density Datasheet 2022.csv")## Rows: 695 Columns: 26
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): site, site_code, date, microsite
## dbl (22): rep, microsite_number, shrub_ID, shrub_number, total_shrub, micros...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Ground_Temp_2022_final <- Ground_Temp_2022 %>%
group_by(site_code, microsite) %>%
summarise(mean_ground_temp = mean(ground_temp), mean_humidity = mean(RH), max_humidity = max(RH), max_ground_temp = max(ground_temp)) ## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.### Combine this with new_data### Aridity Data
aridity <- read_csv("regional_sites_2022.csv")## Rows: 51 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (17): state, desert, region, experiment, site_acronym, sub_site, site_co...
## dbl (7): lat, long, elevation, MAT, MAP, aridity, area_block_m2
## num (1): area_m2
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.aridity <- aridity %>%
dplyr::select(site_code, aridity)final_2022<- photo%>%
group_by(site, site_code, year, microsite, plot, shrub_density, common_name) %>%
summarise(captures = sum(animal.hit), n = n())## `summarise()` has grouped output by 'site', 'site_code', 'year', 'microsite',
## 'plot', 'shrub_density'. You can override using the `.groups` argument.final_2022 <- final_2022 %>%
filter(common_name != "Blank")%>% filter(common_name != "No CV Result")
density_simple <- final_2022 %>%
group_by(site, site_code, year, microsite, plot, shrub_density) %>%
summarise(animals = sum(captures), richness = n()) %>%
rename(microsite_number = 'plot')## `summarise()` has grouped output by 'site', 'site_code', 'year', 'microsite',
## 'plot'. You can override using the `.groups` argument.#write.csv(density_simple, file = "density_simple_fixed.csv") Output density simple because it does not include the sites with 0 observations
density_simple_fixed <- read.csv("density_simple_fixed.csv")
density_simple_fixed <- density_simple_fixed[,-1]### This is for 2022 evenness
vegan_data <- animals_density ### Created new df for pca data
vegan_data <- vegan_data %>%
spread(common_name, captures) %>%
ungroup() %>%
replace(is.na(.),0)
evenness_data <- vegan_data %>%
group_by(site_code, microsite) %>%
summarize(across(5:29, ~diversity(., index = "shannon")))## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.evenness_data <- na.omit(evenness_data)
evenness_data <- evenness_data %>%
mutate(Average_Evenness = rowMeans(across(5:26)))
evenness_data <- evenness_data %>%
dplyr::select(site_code, microsite, Average_Evenness)
### Join evenness_data with density_simple data
new_data <- inner_join(density_simple_fixed, evenness_data, by = c("site_code", "microsite"))
### Combine new data with logger temp data from 2022
new_data <- inner_join(new_data, Temp_2022_final,by = c("site_code", "microsite"))
### Need to add ground temperature from hand recordings then 2022 is ready!
new_data <- inner_join(new_data, Ground_Temp_2022_final, by = c("site_code", "microsite"))
### Combine all data with aridity data of the sites we have
new_data <- inner_join(new_data, aridity, by = c("site_code"))
### 2022 data is now cleaned and ready to go### Clean up 2023 data so it can be properly combined with 2022 data
final_2023<- photo_2023%>%
group_by(site, site_code, year, microsite, plot, shrub_density, common_name) %>%
summarise(captures = sum(animal.hit), n = n())## `summarise()` has grouped output by 'site', 'site_code', 'year', 'microsite',
## 'plot', 'shrub_density'. You can override using the `.groups` argument.density_simple_2023 <- final_2023 %>%
group_by(site, site_code, year, microsite, plot, shrub_density) %>%
summarise(animals = sum(captures), richness = n()) %>%
rename(microsite_number = 'plot')## `summarise()` has grouped output by 'site', 'site_code', 'year', 'microsite',
## 'plot'. You can override using the `.groups` argument.write.csv(density_simple_2023, file = "density_simple_fixed_2023.csv")
### This is for 2023 evenness
vegan_data_2023 <- animals_density_2023 ### Created new df for pca data
vegan_data_2023 <- vegan_data_2023 %>%
spread(common_name, captures) %>%
ungroup() %>%
replace(is.na(.),0)
evenness_data_2023 <- vegan_data_2023 %>%
group_by(site_code, microsite) %>%
summarize(across(5:27, ~diversity(., index = "shannon")))## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.evenness_data_2023 <- na.omit(evenness_data_2023)
evenness_data_2023 <- evenness_data_2023 %>%
mutate(Average_Evenness = rowMeans(across(5:24)))
evenness_data_2023 <- evenness_data_2023 %>%
dplyr::select(site_code, microsite, Average_Evenness)
new_data_2023 <- inner_join(density_simple_2023, evenness_data_2023, by = c("site_code", "microsite"))
new_data_2023$site_code <- gsub("Tecopa_Shrub", "Tecopa_shrub", new_data_2023$site_code)
new_data_2023$site_code <- gsub("Tecopa_Open", "Tecopa_open", new_data_2023$site_code)
### Follow same steps as above 2022 data clean up. Start with logger temp, then ground temp, then aridity.
### Combine new_data_2023 to Temp_2023
Temp_2023 <- read_csv("Temp Data 2023.csv") %>% filter(!(temp > 50)) %>% filter(!(temp < -47))## Rows: 24760 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): researcher, site, site_code, microsite
## dbl (5): microsite_number, pendant_number, pendant_ID, rep, temp
## date (1): date
## time (1): time
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Temp_2023_final <- Temp_2023 %>%
group_by(site_code, microsite) %>%
summarise(mean_temp = mean(temp), max_temp = max(temp))## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.new_data_2023 <- inner_join(new_data_2023, Temp_2023_final, by = c("site_code", "microsite"))
### Combine Ground Temp Data from 2023
Ground_Temp_2023 <- read_csv("Gradient Density Datasheet 2023.csv")## New names:
## Rows: 587 Columns: 29
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (4): site, site_code, date, microsite dbl (25): rep, microsite_number,
## shrub_ID, shrub_number, total_shrub, micros...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...28`
## • `` -> `...29`Ground_Temp_2023_final <- Ground_Temp_2023 %>%
group_by(site_code, microsite, microsite_number) %>%
summarise(mean_ground_temp = mean(ground_temp), mean_humidity = mean(RH), max_humidity = max(RH), max_ground_temp = max(ground_temp)) ## `summarise()` has grouped output by 'site_code', 'microsite'. You can override
## using the `.groups` argument.new_data_2023 <- inner_join(new_data_2023, Ground_Temp_2023_final, by = c("site_code", "microsite", "microsite_number"))
### Aridity data is already set up from 2022 data
new_data_2023 <- inner_join(new_data_2023, aridity, by = c("site_code"))
### 2023 Data is now cleaned and ready to be combined with 2022 datafinal_data <- rbind(new_data, new_data_2023)All Data is combined now into one clean dataframe that can be used to generate figures and statistics (Use final_data)
Stats for Shrub Density
library(ggpubr)
shapiro.test(final_data$animals)##
## Shapiro-Wilk normality test
##
## data: final_data$animals
## W = 0.62948, p-value = 1.551e-09ggqqplot(final_data$animals)
### Stats to show that the aridity across tested regions significantly varies. This needs to be one of the FIRST things you put in your results.
anova_result <- aov(aridity ~ site, data = final_data)
summary(anova_result)## Df Sum Sq Mean Sq F value Pr(>F)
## site 2 103.52 51.76 3639 <2e-16 ***
## Residuals 43 0.61 0.01
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(anova_result, pairwise ~ site)## $emmeans
## site emmean SE df lower.CL upper.CL
## Carrizo 3.252 0.0319 43 3.188 3.316
## Cuyama 3.701 0.0298 43 3.641 3.762
## Tecopa 0.365 0.0298 43 0.305 0.425
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo - Cuyama -0.449 0.0436 43 -10.297 <.0001
## Carrizo - Tecopa 2.887 0.0436 43 66.147 <.0001
## Cuyama - Tecopa 3.336 0.0422 43 79.126 <.0001
##
## P value adjustment: tukey method for comparing a family of 3 estimatestukey_result <- TukeyHSD(anova_result)
# View the Tukey's HSD results
print(tukey_result)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = aridity ~ site, data = final_data)
##
## $site
## diff lwr upr p adj
## Cuyama-Carrizo 0.4494073 0.3434585 0.5553561 0
## Tecopa-Carrizo -2.8870527 -2.9930014 -2.7811039 0
## Tecopa-Cuyama -3.3364600 -3.4388162 -3.2341037 0### Tukey test shows Cuyama less arid than Carrizo, Carrizo less arid than Tecopa, Cuyama less arid than Tecopa### Abundance
model1 <- glm(animals~shrub_density*site*year+aridity, family = "gaussian", data = final_data)
model1##
## Call: glm(formula = animals ~ shrub_density * site * year + aridity,
## family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept) shrub_density
## 7.551e+04 1.061e+04
## siteCuyama siteTecopa
## 4.027e+05 -9.065e+04
## year aridity
## -3.736e+01 3.297e+01
## shrub_density:siteCuyama shrub_density:siteTecopa
## -1.559e+03 -1.211e+04
## shrub_density:year siteCuyama:year
## -5.245e+00 -1.991e+02
## siteTecopa:year shrub_density:siteCuyama:year
## 4.485e+01 7.703e-01
## shrub_density:siteTecopa:year
## 5.986e+00
##
## Degrees of Freedom: 45 Total (i.e. Null); 33 Residual
## Null Deviance: 765800
## Residual Deviance: 302900 AIC: 563anova(model1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 765773
## shrub_density 1 18242 44 747531 0.1586319
## site 2 139832 42 607700 0.0004923 ***
## year 1 137687 41 470013 0.0001076 ***
## aridity 1 55 40 469958 0.9380678
## shrub_density:site 2 3544 38 466414 0.8244495
## shrub_density:year 1 5580 37 460833 0.4355873
## site:year 2 155562 35 305272 0.0002090 ***
## shrub_density:site:year 2 2342 33 302929 0.8802245
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e2 <- emmeans(model1, pairwise~site*year)## NOTE: Results may be misleading due to involvement in interactionse2## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 83.8 122 33 -164 332
## Cuyama 2022 235.1 176 33 -123 593
## Tecopa 2022 74.6 274 33 -482 631
## Carrizo 2023 15.7 111 33 -210 242
## Cuyama 2023 -27.6 176 33 -386 330
## Tecopa 2023 86.4 274 33 -470 643
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 -151.28 73.5 33 -2.057 0.3336
## Carrizo year2022 - Tecopa year2022 9.25 391.4 33 0.024 1.0000
## Carrizo year2022 - Carrizo year2023 68.15 54.7 33 1.246 0.8109
## Carrizo year2022 - Cuyama year2023 111.45 73.5 33 1.516 0.6570
## Carrizo year2022 - Tecopa year2023 -2.59 391.4 33 -0.007 1.0000
## Cuyama year2022 - Tecopa year2022 160.53 446.7 33 0.359 0.9991
## Cuyama year2022 - Carrizo year2023 219.43 87.3 33 2.515 0.1490
## Cuyama year2022 - Cuyama year2023 262.73 47.9 33 5.480 0.0001
## Cuyama year2022 - Tecopa year2023 148.70 446.7 33 0.333 0.9994
## Tecopa year2022 - Carrizo year2023 58.90 378.5 33 0.156 1.0000
## Tecopa year2022 - Cuyama year2023 102.20 446.7 33 0.229 0.9999
## Tecopa year2022 - Tecopa year2023 -11.83 48.2 33 -0.245 0.9999
## Carrizo year2023 - Cuyama year2023 43.30 87.3 33 0.496 0.9960
## Carrizo year2023 - Tecopa year2023 -70.73 378.5 33 -0.187 1.0000
## Cuyama year2023 - Tecopa year2023 -114.04 446.7 33 -0.255 0.9998
##
## P value adjustment: tukey method for comparing a family of 6 estimatesshapiro.test(final_data$richness)##
## Shapiro-Wilk normality test
##
## data: final_data$richness
## W = 0.96052, p-value = 0.1201ggqqplot(final_data$richness)
### Richness Stats
model2 <- glm(richness~shrub_density*site*year +aridity, family = "gaussian", data = final_data)
model2##
## Call: glm(formula = richness ~ shrub_density * site * year + aridity,
## family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept) shrub_density
## -1.049e+03 -9.984e+01
## siteCuyama siteTecopa
## 1.799e+04 -6.515e+03
## year aridity
## 5.227e-01 -5.391e-01
## shrub_density:siteCuyama shrub_density:siteTecopa
## -2.708e+02 -1.922e+02
## shrub_density:year siteCuyama:year
## 4.939e-02 -8.895e+00
## siteTecopa:year shrub_density:siteCuyama:year
## 3.219e+00 1.339e-01
## shrub_density:siteTecopa:year
## 9.508e-02
##
## Degrees of Freedom: 45 Total (i.e. Null); 33 Residual
## Null Deviance: 575.9
## Residual Deviance: 79.32 AIC: 183.6anova_results2 <- aov(model2, test = "Chisq")## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'test' will be disregardedanova(model2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 575.91
## shrub_density 1 27.132 44 548.78 0.0007803 ***
## site 2 164.760 42 384.02 1.305e-15 ***
## year 1 6.218 41 377.80 0.1077656
## aridity 1 1.307 40 376.50 0.4609209
## shrub_density:site 2 1.941 38 374.56 0.6678024
## shrub_density:year 1 3.261 37 371.29 0.2441039
## site:year 2 290.938 35 80.36 < 2.2e-16 ***
## shrub_density:site:year 2 1.034 33 79.32 0.8064261
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(anova_results2)## Df Sum Sq Mean Sq F value Pr(>F)
## shrub_density 1 27.13 27.13 11.287 0.00198 **
## site 2 164.76 82.38 34.272 8.82e-09 ***
## year 1 6.22 6.22 2.587 0.11729
## aridity 1 1.31 1.31 0.544 0.46613
## shrub_density:site 2 1.94 0.97 0.404 0.67106
## shrub_density:year 1 3.26 3.26 1.357 0.25246
## site:year 2 290.94 145.47 60.519 9.11e-12 ***
## shrub_density:site:year 2 1.03 0.52 0.215 0.80755
## Residuals 33 79.32 2.40
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e3 <- emmeans(model2, pairwise~site*year)## NOTE: Results may be misleading due to involvement in interactionse3 ### Ok now this all works.## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 6.356 1.97 33 2.346 10.37
## Cuyama 2022 12.443 2.85 33 6.649 18.24
## Tecopa 2022 0.208 4.43 33 -8.799 9.21
## Carrizo 2023 7.168 1.80 33 3.513 10.82
## Cuyama 2023 5.146 2.85 33 -0.648 10.94
## Tecopa 2023 4.797 4.43 33 -4.209 13.80
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 -6.087 1.190 33 -5.116 0.0002
## Carrizo year2022 - Tecopa year2022 6.148 6.333 33 0.971 0.9239
## Carrizo year2022 - Carrizo year2023 -0.813 0.885 33 -0.918 0.9390
## Carrizo year2022 - Cuyama year2023 1.210 1.190 33 1.017 0.9090
## Carrizo year2022 - Tecopa year2023 1.558 6.333 33 0.246 0.9999
## Cuyama year2022 - Tecopa year2022 12.235 7.229 33 1.692 0.5461
## Cuyama year2022 - Carrizo year2023 5.274 1.412 33 3.736 0.0085
## Cuyama year2022 - Cuyama year2023 7.297 0.776 33 9.405 <.0001
## Cuyama year2022 - Tecopa year2023 7.645 7.229 33 1.058 0.8944
## Tecopa year2022 - Carrizo year2023 -6.960 6.125 33 -1.136 0.8626
## Tecopa year2022 - Cuyama year2023 -4.938 7.229 33 -0.683 0.9826
## Tecopa year2022 - Tecopa year2023 -4.590 0.781 33 -5.880 <.0001
## Carrizo year2023 - Cuyama year2023 2.022 1.412 33 1.432 0.7076
## Carrizo year2023 - Tecopa year2023 2.371 6.125 33 0.387 0.9988
## Cuyama year2023 - Tecopa year2023 0.348 7.229 33 0.048 1.0000
##
## P value adjustment: tukey method for comparing a family of 6 estimatesggqqplot(final_data$Average_Evenness)
### Evenness
model3 <- glm(Average_Evenness~shrub_density*site*year + aridity, family = "gaussian", data = final_data)
model3##
## Call: glm(formula = Average_Evenness ~ shrub_density * site * year +
## aridity, family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept) shrub_density
## 1.175e+02 -8.606e+00
## siteCuyama siteTecopa
## 2.013e+02 -3.866e+02
## year aridity
## -5.801e-02 -2.568e-02
## shrub_density:siteCuyama shrub_density:siteTecopa
## 1.008e+01 -9.806e+00
## shrub_density:year siteCuyama:year
## 4.257e-03 -9.952e-02
## siteTecopa:year shrub_density:siteCuyama:year
## 1.911e-01 -4.981e-03
## shrub_density:siteTecopa:year
## 4.848e-03
##
## Degrees of Freedom: 45 Total (i.e. Null); 33 Residual
## Null Deviance: 0.3064
## Residual Deviance: 0.02358 AIC: -190anova_results3 <- aov(model3, test = "Chisq")## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'test' will be disregardedanova(model3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 0.306353
## shrub_density 1 0.020430 44 0.285923 8.936e-08 ***
## site 2 0.011457 42 0.274466 0.0003297 ***
## year 1 0.000058 41 0.274408 0.7763951
## aridity 1 0.000022 40 0.274386 0.8618492
## shrub_density:site 2 0.001475 38 0.272911 0.3562435
## shrub_density:year 1 0.000932 37 0.271980 0.2535206
## site:year 2 0.242134 35 0.029845 < 2.2e-16 ***
## shrub_density:site:year 2 0.006265 33 0.023580 0.0124756 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(anova_results3)## Df Sum Sq Mean Sq F value Pr(>F)
## shrub_density 1 0.02043 0.02043 28.592 6.62e-06 ***
## site 2 0.01146 0.00573 8.017 0.00145 **
## year 1 0.00006 0.00006 0.081 0.77817
## aridity 1 0.00002 0.00002 0.030 0.86291
## shrub_density:site 2 0.00148 0.00074 1.032 0.36746
## shrub_density:year 1 0.00093 0.00093 1.304 0.26174
## site:year 2 0.24213 0.12107 169.432 < 2e-16 ***
## shrub_density:site:year 2 0.00627 0.00313 4.384 0.02049 *
## Residuals 33 0.02358 0.00071
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e4 <- emmeans(model3, pairwise~site*year)## NOTE: Results may be misleading due to involvement in interactionse4 ### Need to double check emmeans it does not match the figure## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 0.1186 0.0340 33 0.04944 0.188
## Cuyama 2022 0.2302 0.0491 33 0.13028 0.330
## Tecopa 2022 -0.0251 0.0763 33 -0.18038 0.130
## Carrizo 2023 0.0856 0.0310 33 0.02254 0.149
## Cuyama 2023 0.0684 0.0491 33 -0.03150 0.168
## Tecopa 2023 0.1615 0.0763 33 0.00617 0.317
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 -0.1116 0.0205 33 -5.440 0.0001
## Carrizo year2022 - Tecopa year2022 0.1437 0.1092 33 1.316 0.7744
## Carrizo year2022 - Carrizo year2023 0.0330 0.0153 33 2.165 0.2809
## Carrizo year2022 - Cuyama year2023 0.0502 0.0205 33 2.446 0.1701
## Carrizo year2022 - Tecopa year2023 -0.0429 0.1092 33 -0.393 0.9987
## Cuyama year2022 - Tecopa year2022 0.2553 0.1246 33 2.048 0.3384
## Cuyama year2022 - Carrizo year2023 0.1446 0.0243 33 5.941 <.0001
## Cuyama year2022 - Cuyama year2023 0.1618 0.0134 33 12.094 <.0001
## Cuyama year2022 - Tecopa year2023 0.0687 0.1246 33 0.551 0.9934
## Tecopa year2022 - Carrizo year2023 -0.1106 0.1056 33 -1.048 0.8980
## Tecopa year2022 - Cuyama year2023 -0.0935 0.1246 33 -0.750 0.9738
## Tecopa year2022 - Tecopa year2023 -0.1865 0.0135 33 -13.861 <.0001
## Carrizo year2023 - Cuyama year2023 0.0172 0.0243 33 0.705 0.9800
## Carrizo year2023 - Tecopa year2023 -0.0759 0.1056 33 -0.719 0.9782
## Cuyama year2023 - Tecopa year2023 -0.0931 0.1246 33 -0.747 0.9743
##
## P value adjustment: tukey method for comparing a family of 6 estimatesData Viz
### All below in chunk are by site
### Abundance vs Density
abundance <- ggplot(final_data, aes(shrub_density, animals, color = site)) +
geom_point(size = 0.5) +
facet_wrap(~year)+
scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "A")+
labs(x = "Shrub Density (Individuals per 20m radius)", y = "Animal Abundance")
abundance ## `geom_smooth()` using formula = 'y ~ x'
richness <- ggplot(final_data, aes(shrub_density, richness, color = site)) +
geom_point(size = 0.5) +
facet_wrap(~year)+
scale_color_brewer(palette = "Set1") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "B")+
labs(x = "Shrub Density (Individuals per 20m radius)", y = "Richness", color = "Region")
richness## `geom_smooth()` using formula = 'y ~ x'
Evenness <- ggplot(final_data, aes(shrub_density, Average_Evenness, color = site)) +
geom_point(size = 0.5) +
facet_wrap(~year)+
scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + labs(tag = "C")+
labs(x = expression("Shrub Density per " * 20 * m^2), y = "Mean Evenness")
Evenness## `geom_smooth()` using formula = 'y ~ x'
library(patchwork)##
## Attaching package: 'patchwork'
##
## The following object is masked from 'package:MASS':
##
## areadensity_plot <- abundance/richness/Evenness
density_plot## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
### Stats for Average Temperature
### Abundance and temperature
model4 <- glm(animals~mean_temp*site*year*mean_humidity, family = "gaussian", data = final_data)
model4##
## Call: glm(formula = animals ~ mean_temp * site * year * mean_humidity,
## family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept)
## -6.423e+08
## mean_temp
## 2.677e+07
## siteCuyama
## 4.148e+08
## siteTecopa
## -4.661e+08
## year
## 3.175e+05
## mean_humidity
## 2.602e+07
## mean_temp:siteCuyama
## -1.626e+07
## mean_temp:siteTecopa
## 1.146e+07
## mean_temp:year
## -1.323e+04
## siteCuyama:year
## -2.051e+05
## siteTecopa:year
## 2.304e+05
## mean_temp:mean_humidity
## -1.084e+06
## siteCuyama:mean_humidity
## -1.764e+07
## siteTecopa:mean_humidity
## -7.572e+02
## year:mean_humidity
## -1.286e+04
## mean_temp:siteCuyama:year
## 8.037e+03
## mean_temp:siteTecopa:year
## -5.664e+03
## mean_temp:siteCuyama:mean_humidity
## 6.983e+05
## mean_temp:siteTecopa:mean_humidity
## 2.925e+01
## mean_temp:year:mean_humidity
## 5.360e+02
## siteCuyama:year:mean_humidity
## 8.722e+03
## siteTecopa:year:mean_humidity
## NA
## mean_temp:siteCuyama:year:mean_humidity
## -3.452e+02
## mean_temp:siteTecopa:year:mean_humidity
## NA
##
## Degrees of Freedom: 45 Total (i.e. Null); 24 Residual
## Null Deviance: 765800
## Residual Deviance: 57540 AIC: 504.6anova_results4 <- aov(model4, test = "Chisq")## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'test' will be disregardedanova(model4, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 765773
## mean_temp 1 69722 44 696052 6.934e-08
## site 2 82375 42 613676 3.456e-08
## year 1 184771 41 428905 < 2.2e-16
## mean_humidity 1 1138 40 427767 0.4907466
## mean_temp:site 2 108585 38 319182 1.461e-10
## mean_temp:year 1 7837 37 311345 0.0706052
## site:year 2 33153 35 278192 0.0009931
## mean_temp:mean_humidity 1 962 34 277230 0.5264108
## site:mean_humidity 2 5743 32 271487 0.3018453
## year:mean_humidity 1 7165 31 264322 0.0838464
## mean_temp:site:year 2 6207 29 258114 0.2739932
## mean_temp:site:mean_humidity 2 160588 27 97526 2.845e-15
## mean_temp:year:mean_humidity 1 367 26 97159 0.6956019
## site:year:mean_humidity 1 36297 25 60862 9.978e-05
## mean_temp:site:year:mean_humidity 1 3326 24 57536 0.2388432
##
## NULL
## mean_temp ***
## site ***
## year ***
## mean_humidity
## mean_temp:site ***
## mean_temp:year .
## site:year ***
## mean_temp:mean_humidity
## site:mean_humidity
## year:mean_humidity .
## mean_temp:site:year
## mean_temp:site:mean_humidity ***
## mean_temp:year:mean_humidity
## site:year:mean_humidity ***
## mean_temp:site:year:mean_humidity
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(anova_results4)## Df Sum Sq Mean Sq F value Pr(>F)
## mean_temp 1 69722 69722 28.639 1.50e-05 ***
## site 2 82375 41188 16.919 2.26e-05 ***
## year 1 184771 184771 75.898 4.80e-09 ***
## mean_humidity 1 1138 1138 0.468 0.500364
## mean_temp:site 2 108585 54292 22.301 2.76e-06 ***
## mean_temp:year 1 7837 7837 3.219 0.084892 .
## site:year 2 33153 16577 6.809 0.004358 **
## mean_temp:mean_humidity 1 962 962 0.395 0.535285
## site:mean_humidity 2 5743 2872 1.180 0.323946
## year:mean_humidity 1 7165 7165 2.943 0.098614 .
## mean_temp:site:year 2 6207 3104 1.275 0.297010
## mean_temp:site:mean_humidity 2 160588 80294 32.982 9.74e-08 ***
## mean_temp:year:mean_humidity 1 367 367 0.151 0.701105
## site:year:mean_humidity 1 36297 36297 14.910 0.000707 ***
## Residuals 25 60862 2434
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e5 <- emmeans(model4, pairwise~site*year)## NOTE: Results may be misleading due to involvement in interactionse5## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 -938.5 619 24 -2217 340
## Cuyama 2022 1714.0 146 24 1414 2015
## Tecopa 2022 -89833.5 55883 24 -205170 25503
## Carrizo 2023 37.5 261 24 -501 576
## Cuyama 2023 1006.2 1997 24 -3116 5128
## Tecopa 2023 -81.9 3323 24 -6940 6776
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 -2652 636 24 -4.170 0.0041
## Carrizo year2022 - Tecopa year2022 88895 55335 24 1.606 0.6024
## Carrizo year2022 - Carrizo year2023 -976 672 24 -1.453 0.6959
## Carrizo year2022 - Cuyama year2023 -1945 2091 24 -0.930 0.9347
## Carrizo year2022 - Tecopa year2023 -857 3380 24 -0.253 0.9998
## Cuyama year2022 - Tecopa year2022 91548 55883 24 1.638 0.5828
## Cuyama year2022 - Carrizo year2023 1676 299 24 5.615 0.0001
## Cuyama year2022 - Cuyama year2023 708 2003 24 0.353 0.9992
## Cuyama year2022 - Tecopa year2023 1796 3326 24 0.540 0.9938
## Tecopa year2022 - Carrizo year2023 -89871 55876 24 -1.608 0.6012
## Tecopa year2022 - Cuyama year2023 -90840 55919 24 -1.624 0.5913
## Tecopa year2022 - Tecopa year2023 -89752 57406 24 -1.563 0.6288
## Carrizo year2023 - Cuyama year2023 -969 2014 24 -0.481 0.9964
## Carrizo year2023 - Tecopa year2023 119 3333 24 0.036 1.0000
## Cuyama year2023 - Tecopa year2023 1088 3877 24 0.281 0.9997
##
## P value adjustment: tukey method for comparing a family of 6 estimates### Richness and temperature
model5 <- glm(richness~mean_temp*site*year*mean_humidity, family = "gaussian", data = final_data)
model5##
## Call: glm(formula = richness ~ mean_temp * site * year * mean_humidity,
## family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept)
## 4.250e+05
## mean_temp
## -2.300e+04
## siteCuyama
## 8.940e+06
## siteTecopa
## 1.097e+05
## year
## -2.101e+02
## mean_humidity
## -1.721e+04
## mean_temp:siteCuyama
## -3.999e+05
## mean_temp:siteTecopa
## 5.848e+03
## mean_temp:year
## 1.137e+01
## siteCuyama:year
## -4.421e+03
## siteTecopa:year
## -5.411e+01
## mean_temp:mean_humidity
## 9.181e+02
## siteCuyama:mean_humidity
## -2.453e+05
## siteTecopa:mean_humidity
## 4.210e+00
## year:mean_humidity
## 8.507e+00
## mean_temp:siteCuyama:year
## 1.977e+02
## mean_temp:siteTecopa:year
## -2.896e+00
## mean_temp:siteCuyama:mean_humidity
## 1.097e+04
## mean_temp:siteTecopa:mean_humidity
## -3.693e-02
## mean_temp:year:mean_humidity
## -4.539e-01
## siteCuyama:year:mean_humidity
## 1.213e+02
## siteTecopa:year:mean_humidity
## NA
## mean_temp:siteCuyama:year:mean_humidity
## -5.424e+00
## mean_temp:siteTecopa:year:mean_humidity
## NA
##
## Degrees of Freedom: 45 Total (i.e. Null); 24 Residual
## Null Deviance: 575.9
## Residual Deviance: 21.07 AIC: 140.6anova_results5 <- aov(model5, test = "Chisq")## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'test' will be disregardedanova(model5, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 575.91
## mean_temp 1 212.116 44 363.80 < 2.2e-16
## site 2 13.679 42 350.12 0.0004135
## year 1 101.722 41 248.40 < 2.2e-16
## mean_humidity 1 36.989 40 211.41 8.530e-11
## mean_temp:site 2 105.199 38 106.21 < 2.2e-16
## mean_temp:year 1 22.336 37 83.87 4.559e-07
## site:year 2 17.597 35 66.28 4.442e-05
## mean_temp:mean_humidity 1 1.362 34 64.91 0.2129439
## site:mean_humidity 2 6.395 32 58.52 0.0261978
## year:mean_humidity 1 0.241 31 58.28 0.6000577
## mean_temp:site:year 2 7.846 29 50.43 0.0114660
## mean_temp:site:mean_humidity 2 3.530 27 46.90 0.1339519
## mean_temp:year:mean_humidity 1 16.254 26 30.65 1.686e-05
## site:year:mean_humidity 1 8.757 25 21.89 0.0015875
## mean_temp:site:year:mean_humidity 1 0.821 24 21.07 0.3334469
##
## NULL
## mean_temp ***
## site ***
## year ***
## mean_humidity ***
## mean_temp:site ***
## mean_temp:year ***
## site:year ***
## mean_temp:mean_humidity
## site:mean_humidity *
## year:mean_humidity
## mean_temp:site:year *
## mean_temp:site:mean_humidity
## mean_temp:year:mean_humidity ***
## site:year:mean_humidity **
## mean_temp:site:year:mean_humidity
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(anova_results5)## Df Sum Sq Mean Sq F value Pr(>F)
## mean_temp 1 212.12 212.12 242.238 2.27e-14 ***
## site 2 13.68 6.84 7.811 0.002316 **
## year 1 101.72 101.72 116.168 6.92e-11 ***
## mean_humidity 1 36.99 36.99 42.241 8.30e-07 ***
## mean_temp:site 2 105.20 52.60 60.069 2.83e-10 ***
## mean_temp:year 1 22.34 22.34 25.508 3.27e-05 ***
## site:year 2 17.60 8.80 10.048 0.000627 ***
## mean_temp:mean_humidity 1 1.36 1.36 1.555 0.223910
## site:mean_humidity 2 6.39 3.20 3.652 0.040617 *
## year:mean_humidity 1 0.24 0.24 0.276 0.604211
## mean_temp:site:year 2 7.85 3.92 4.480 0.021736 *
## mean_temp:site:mean_humidity 2 3.53 1.76 2.015 0.154342
## mean_temp:year:mean_humidity 1 16.25 16.25 18.563 0.000224 ***
## site:year:mean_humidity 1 8.76 8.76 10.000 0.004075 **
## Residuals 25 21.89 0.88
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e6 <- emmeans(model5, pairwise~site*year)## NOTE: Results may be misleading due to involvement in interactionse6## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 12.10 11.85 24 -12.360 36.56
## Cuyama 2022 1.88 2.80 24 -3.898 7.67
## Tecopa 2022 161.29 1069.41 24 -2045.856 2368.43
## Carrizo 2023 10.67 4.99 24 0.371 20.97
## Cuyama 2023 142.87 38.22 24 63.983 221.75
## Tecopa 2023 33.32 63.59 24 -97.914 164.55
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 10.22 12.17 24 0.839 0.9570
## Carrizo year2022 - Tecopa year2022 -149.19 1058.93 24 -0.141 1.0000
## Carrizo year2022 - Carrizo year2023 1.43 12.86 24 0.111 1.0000
## Carrizo year2022 - Cuyama year2023 -130.77 40.02 24 -3.268 0.0340
## Carrizo year2022 - Tecopa year2023 -21.22 64.68 24 -0.328 0.9994
## Cuyama year2022 - Tecopa year2022 -159.41 1069.41 24 -0.149 1.0000
## Cuyama year2022 - Carrizo year2023 -8.79 5.71 24 -1.538 0.6444
## Cuyama year2022 - Cuyama year2023 -140.98 38.32 24 -3.679 0.0133
## Cuyama year2022 - Tecopa year2023 -31.44 63.65 24 -0.494 0.9959
## Tecopa year2022 - Carrizo year2023 150.62 1069.27 24 0.141 1.0000
## Tecopa year2022 - Cuyama year2023 18.42 1070.09 24 0.017 1.0000
## Tecopa year2022 - Tecopa year2023 127.97 1098.55 24 0.116 1.0000
## Carrizo year2023 - Cuyama year2023 -132.19 38.54 24 -3.430 0.0236
## Carrizo year2023 - Tecopa year2023 -22.65 63.78 24 -0.355 0.9992
## Cuyama year2023 - Tecopa year2023 109.55 74.19 24 1.477 0.6816
##
## P value adjustment: tukey method for comparing a family of 6 estimates### Evenness and temperature
model6 <- glm(Average_Evenness~mean_temp*site*year*mean_humidity, family = "gaussian", data = final_data)
model6##
## Call: glm(formula = Average_Evenness ~ mean_temp * site * year * mean_humidity,
## family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept)
## -2.924e+04
## mean_temp
## 1.075e+03
## siteCuyama
## 1.978e+05
## siteTecopa
## 1.918e+04
## year
## 1.445e+01
## mean_humidity
## 1.089e+03
## mean_temp:siteCuyama
## -8.739e+03
## mean_temp:siteTecopa
## -7.814e+02
## mean_temp:year
## -5.312e-01
## siteCuyama:year
## -9.782e+01
## siteTecopa:year
## -9.486e+00
## mean_temp:mean_humidity
## -3.940e+01
## siteCuyama:mean_humidity
## -6.112e+03
## siteTecopa:mean_humidity
## -1.112e-02
## year:mean_humidity
## -5.383e-01
## mean_temp:siteCuyama:year
## 4.321e+00
## mean_temp:siteTecopa:year
## 3.864e-01
## mean_temp:siteCuyama:mean_humidity
## 2.686e+02
## mean_temp:siteTecopa:mean_humidity
## 5.490e-04
## mean_temp:year:mean_humidity
## 1.948e-02
## siteCuyama:year:mean_humidity
## 3.022e+00
## siteTecopa:year:mean_humidity
## NA
## mean_temp:siteCuyama:year:mean_humidity
## -1.328e-01
## mean_temp:siteTecopa:year:mean_humidity
## NA
##
## Degrees of Freedom: 45 Total (i.e. Null); 24 Residual
## Null Deviance: 0.3064
## Residual Deviance: 0.001288 AIC: -305.7anova_results6 <- aov(model6, test = "Chisq")## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'test' will be disregardedanova(model6, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 0.306353
## mean_temp 1 0.011046 44 0.295307 < 2.2e-16
## site 2 0.073313 42 0.221994 < 2.2e-16
## year 1 0.068416 41 0.153577 < 2.2e-16
## mean_humidity 1 0.004958 40 0.148620 < 2.2e-16
## mean_temp:site 2 0.112108 38 0.036512 < 2.2e-16
## mean_temp:year 1 0.002065 37 0.034447 5.578e-10
## site:year 2 0.012030 35 0.022417 < 2.2e-16
## mean_temp:mean_humidity 1 0.000145 34 0.022272 0.1001135
## site:mean_humidity 2 0.000774 32 0.021498 0.0007366
## year:mean_humidity 1 0.001450 31 0.020048 2.032e-07
## mean_temp:site:year 2 0.003008 29 0.017040 6.819e-13
## mean_temp:site:mean_humidity 2 0.005407 27 0.011633 < 2.2e-16
## mean_temp:year:mean_humidity 1 0.007064 26 0.004569 < 2.2e-16
## site:year:mean_humidity 1 0.002788 25 0.001781 5.712e-13
## mean_temp:site:year:mean_humidity 1 0.000492 24 0.001288 0.0024604
##
## NULL
## mean_temp ***
## site ***
## year ***
## mean_humidity ***
## mean_temp:site ***
## mean_temp:year ***
## site:year ***
## mean_temp:mean_humidity
## site:mean_humidity ***
## year:mean_humidity ***
## mean_temp:site:year ***
## mean_temp:site:mean_humidity ***
## mean_temp:year:mean_humidity ***
## site:year:mean_humidity ***
## mean_temp:site:year:mean_humidity **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(anova_results6)## Df Sum Sq Mean Sq F value Pr(>F)
## mean_temp 1 0.01105 0.01105 155.089 3.23e-12 ***
## site 2 0.07331 0.03666 514.655 < 2e-16 ***
## year 1 0.06842 0.06842 960.555 < 2e-16 ***
## mean_humidity 1 0.00496 0.00496 69.605 1.08e-08 ***
## mean_temp:site 2 0.11211 0.05605 786.988 < 2e-16 ***
## mean_temp:year 1 0.00206 0.00206 28.990 1.38e-05 ***
## site:year 2 0.01203 0.00602 84.452 7.58e-12 ***
## mean_temp:mean_humidity 1 0.00015 0.00015 2.038 0.165804
## site:mean_humidity 2 0.00077 0.00039 5.437 0.010954 *
## year:mean_humidity 1 0.00145 0.00145 20.352 0.000132 ***
## mean_temp:site:year 2 0.00301 0.00150 21.114 4.26e-06 ***
## mean_temp:site:mean_humidity 2 0.00541 0.00270 37.958 2.66e-08 ***
## mean_temp:year:mean_humidity 1 0.00706 0.00706 99.178 3.48e-10 ***
## site:year:mean_humidity 1 0.00279 0.00279 39.150 1.52e-06 ***
## Residuals 25 0.00178 0.00007
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e7 <- emmeans(model6, pairwise~site*year)## NOTE: Results may be misleading due to involvement in interactionse7## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 0.2333 0.0927 24 0.0420 0.4246
## Cuyama 2022 -0.0603 0.0218 24 -0.1054 -0.0153
## Tecopa 2022 -0.0817 8.3624 24 -17.3409 17.1775
## Carrizo 2023 0.0399 0.0390 24 -0.0407 0.1204
## Cuyama 2023 2.0244 0.2989 24 1.4075 2.6413
## Tecopa 2023 -0.0993 0.4972 24 -1.1255 0.9269
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 0.2937 0.0952 24 3.084 0.0508
## Carrizo year2022 - Tecopa year2022 0.3150 8.2805 24 0.038 1.0000
## Carrizo year2022 - Carrizo year2023 0.1935 0.1005 24 1.924 0.4125
## Carrizo year2022 - Cuyama year2023 -1.7911 0.3129 24 -5.724 0.0001
## Carrizo year2022 - Tecopa year2023 0.3326 0.5058 24 0.658 0.9849
## Cuyama year2022 - Tecopa year2022 0.0213 8.3625 24 0.003 1.0000
## Cuyama year2022 - Carrizo year2023 -0.1002 0.0447 24 -2.241 0.2565
## Cuyama year2022 - Cuyama year2023 -2.0848 0.2997 24 -6.957 <.0001
## Cuyama year2022 - Tecopa year2023 0.0390 0.4977 24 0.078 1.0000
## Tecopa year2022 - Carrizo year2023 -0.1215 8.3614 24 -0.015 1.0000
## Tecopa year2022 - Cuyama year2023 -2.1061 8.3678 24 -0.252 0.9998
## Tecopa year2022 - Tecopa year2023 0.0176 8.5903 24 0.002 1.0000
## Carrizo year2023 - Cuyama year2023 -1.9846 0.3014 24 -6.584 <.0001
## Carrizo year2023 - Tecopa year2023 0.1392 0.4987 24 0.279 0.9997
## Cuyama year2023 - Tecopa year2023 2.1237 0.5801 24 3.661 0.0139
##
## P value adjustment: tukey method for comparing a family of 6 estimates### Ambient Temperature Plots
### Plots of temp might look super ugly.
abundance_temp <- ggplot(final_data, aes(mean_temp, animals)) +
geom_point(size = 0.5) +
facet_wrap(~year, scales = "free")+
scale_color_brewer(palette = "Set1") +
labs(x = "Average Temperature (C)", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none",legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "A")
abundance_temp## `geom_smooth()` using formula = 'y ~ x'
richness_temp <- ggplot(final_data, aes(mean_temp, richness)) +
geom_point(size = 0.5) +
facet_wrap(~year, scales = "free")+
scale_color_brewer(palette = "Set1") + theme(axis.title.x = element_blank()) + labs(tag = "B")+ theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none",legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Average Temperature (C)", y = "Richness") + theme(axis.title.x = element_blank())
richness_temp## `geom_smooth()` using formula = 'y ~ x'
evenness_temp <- ggplot(final_data, aes(mean_temp, Average_Evenness)) +
geom_point(size = 0.5) +
facet_wrap(~year, scales = "free")+
scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + labs(tag = "C")+
labs(x = expression("Mean Temperature (°C)"), y = "Mean Evenness")
evenness_temp## `geom_smooth()` using formula = 'y ~ x'
temp_plot <- abundance_temp/richness_temp/evenness_temp
temp_plot## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
### Other posisble figure options
abundance_2 <- ggplot(final_data, aes(shrub_density, animals)) + geom_point(size = 0.5) +
facet_wrap(~year, scales = "free") + scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "A")+
labs(x = "Shrub Density (Individuals per 20m radius)", y = "Animal Abundance")
abundance_2## `geom_smooth()` using formula = 'y ~ x'
richness_2 <- ggplot(final_data, aes(shrub_density, richness)) + geom_point(size = 0.5) +
facet_wrap(~year, scales = "free") + scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "B")+
labs(x = "Shrub Density (Individuals per 20m radius)", y = "Richness")
richness_2## `geom_smooth()` using formula = 'y ~ x'
evenness_2 <- ggplot(final_data, aes(shrub_density, Average_Evenness)) + geom_point(size = 0.5) +
facet_wrap(~year, scales = "free") + scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + labs(tag = "C")+
labs(x = expression("Shrub Density per " * 20 * m^2), y = "Mean Evenness")
plot1.1 <- abundance_2/richness_2/evenness_2
plot1.1## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
### Percent proportion Figure
#write.csv(density_obvs_final, file = "Animal Observations.csv")
scientific_names <- read.csv("Animal Observations.csv")
plot3 <- ggplot(scientific_names, aes(scientific_name, percent_presence, fill = microsite)) + geom_bar(stat = "identity") + coord_flip() + theme_classic() + scale_x_discrete(limits=rev) + xlab("Species") + ylab("Percent Proportion") + labs(fill = "Microsite")
plot3 <-plot3 + scale_fill_manual(values = c("#009900", "#0066cc"))# Assuming you have a dataset named scientific_names with columns: scientific_name, percent_presence, microsite
# Create a new variable to specify the ordering based on presence in density or open areas
scientific_names$microsite <- ifelse(scientific_names$microsite == "Density", "Shrub", scientific_names$microsite)
scientific_names <- scientific_names %>%
mutate(ordering_var = ifelse(microsite == "Shrub", 1, 2)) %>%
arrange(ordering_var, desc(percent_presence))
# Create the histogram-style figure with reordering
plot3.2 <- ggplot(scientific_names, aes(fct_inorder(scientific_name), percent_presence, fill = microsite)) +
geom_bar(stat = "identity") +
coord_flip() +
theme_classic() +
xlab("Species") +
ylab("Percent Proportion") +
labs(fill = "Microsite") +
scale_fill_manual(values = c("#0066cc", "#009900")) + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10))
# Reorder the x-axis labels
plot3.2 <- plot3.2 + scale_x_discrete(limits = rev(levels(scientific_names$scientific_name)))
# Plot the figure
print(plot3.2)
Final Figures for Publication
### Figure 1: Abundance, Richness, Evenness vs Shrub Density
plot1.1## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
### Figure 2: Abundance, Richness, Evenness vs Average Temperature
temp_plot## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
### Figure 3: PCOA of Communities
pcoa_final
### Figure 4: Percent Proportion of Vertebrate species
plot3.2
#write.csv(final_data, file = "Final Data.csv")Data Together
### Test figure with formula (Shrub Density)
final_data <- final_data %>%
mutate(year = as.character(year))
### Density v abundance
ggplot(final_data, aes(shrub_density, animals, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "A")
### Density v Richness
ggplot(final_data, aes(shrub_density, richness, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Richness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "B")
### Density v Evenness
ggplot(final_data, aes(shrub_density, Average_Evenness, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Evenness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "C")
### Aridity v Abundance
ggplot(final_data, aes(aridity, animals, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Aridity", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "A")
### Aridity v Richness
ggplot(final_data, aes(aridity, richness, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Aridity", y = "Richness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "B")
### Aridity v Evenness
ggplot(final_data, aes(aridity, Average_Evenness, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Aridity", y = "Evenness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "C")
### Stats
#install.packages("lme4", type = "source")
library(lmerTest)## Loading required package: lme4## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
##
## steplibrary(performance)
#simple
m1 <- lmer(animals ~ shrub_density + (1|year) + aridity, data = final_data)
check_collinearity(m1)## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
## shrub_density 1.01 [1.00, 2.65e+12] 1.00 0.99 [0.00, 1.00]
## aridity 1.01 [1.00, 2.65e+12] 1.00 0.99 [0.00, 1.00]anova(m1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## shrub_density 14370 14370 1 42.018 1.2164 0.27635
## aridity 114226 114226 1 42.010 9.6692 0.00336 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lsmeansLT(m1)## Least Squares Means table:
##
## Estimate Std. Error df t value lower upper Pr(>|t|)
##
## Confidence level: 95%
## Degrees of freedom method: Satterthwaiteranova(m1) # I think this is to test for random effects across year?## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## animals ~ shrub_density + aridity + (1 | year)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 -271.70 553.40
## (1 | year) 4 -274.97 557.94 6.5368 1 0.01057 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m2 <- lmer(richness ~ shrub_density + (1|year) + aridity, data = final_data)## boundary (singular) fit: see help('isSingular')check_collinearity(m2)## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
## shrub_density 1.01 [1.00, 7.65e+12] 1.00 0.99 [0.00, 1.00]
## aridity 1.01 [1.00, 7.65e+12] 1.00 0.99 [0.00, 1.00]anova(m2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## shrub_density 17.096 17.096 1 43 1.8397 0.1820664
## aridity 149.184 149.184 1 43 16.0534 0.0002401 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ranova(m2)## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## richness ~ shrub_density + aridity + (1 | year)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 -116.83 243.66
## (1 | year) 4 -116.83 241.66 -8.5265e-14 1 1m3 <- lmer(Average_Evenness ~ shrub_density + (1|year) + aridity, data = final_data)## boundary (singular) fit: see help('isSingular')check_collinearity(m3)## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
## shrub_density 1.01 [1.00, 7.65e+12] 1.00 0.99 [0.00, 1.00]
## aridity 1.01 [1.00, 7.65e+12] 1.00 0.99 [0.00, 1.00]anova(m3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## shrub_density 0.0215532 0.0215532 1 43 3.2712 0.0775 .
## aridity 0.0026079 0.0026079 1 43 0.3958 0.5326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ranova(m3)## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## Average_Evenness ~ shrub_density + aridity + (1 | year)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 39.082 -68.164
## (1 | year) 4 39.082 -70.164 -1.4211e-14 1 1Test Trophic Level?
trophic <- read.csv("Animal Observations.csv")
ggplot(trophic, aes(microsite, captures, color = Trophic)) +
geom_boxplot() +
scale_color_brewer(palette = "Set1") + labs(x = "Aridity", y = "Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "C")
m9<- glm(total ~ microsite * Trophic, family = "poisson", data = trophic)
anova(m9, test = "Chisq")## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: total
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 55 27821
## microsite 1 29.6 54 27791 5.449e-08 ***
## Trophic 2 7884.0 52 19907 < 2.2e-16 ***
## microsite:Trophic 2 2.0 50 19905 0.376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e9 <- emmeans(m9, pairwise~microsite|Trophic)
e9## $emmeans
## Trophic = Carnivore:
## microsite emmean SE df asymp.LCL asymp.UCL
## Density 3.06 0.0767 Inf 2.91 3.21
## Open 3.18 0.0769 Inf 3.03 3.33
##
## Trophic = Herbivore:
## microsite emmean SE df asymp.LCL asymp.UCL
## Density 5.62 0.0173 Inf 5.59 5.66
## Open 5.71 0.0174 Inf 5.68 5.74
##
## Trophic = Omnivore:
## microsite emmean SE df asymp.LCL asymp.UCL
## Density 3.06 0.0685 Inf 2.92 3.19
## Open 3.00 0.0788 Inf 2.85 3.16
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## Trophic = Carnivore:
## contrast estimate SE df z.ratio p.value
## Density - Open -0.1276 0.1086 Inf -1.175 0.2400
##
## Trophic = Herbivore:
## contrast estimate SE df z.ratio p.value
## Density - Open -0.0849 0.0245 Inf -3.461 0.0005
##
## Trophic = Omnivore:
## contrast estimate SE df z.ratio p.value
## Density - Open 0.0567 0.1044 Inf 0.543 0.5869
##
## Results are given on the log (not the response) scale.Seperate everything by year (Nonlinear better for 2022)
final_data_2022 <- final_data %>%
filter(year == "2022")
shapiro.test(final_data_2022$animals)##
## Shapiro-Wilk normality test
##
## data: final_data_2022$animals
## W = 0.76017, p-value = 7.037e-05ggqqplot(final_data_2022$animals)
n1 <- glm(animals ~ shrub_density * aridity, family = "gaussian", data = final_data_2022)
n1##
## Call: glm(formula = animals ~ shrub_density * aridity, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density aridity
## -27.7891 0.6522 54.0811
## shrub_density:aridity
## 1.6648
##
## Degrees of Freedom: 23 Total (i.e. Null); 20 Residual
## Null Deviance: 619900
## Residual Deviance: 376700 AIC: 310anova(n1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 619927
## shrub_density 1 26978 22 592949 0.2314117
## aridity 1 211738 21 381211 0.0008004 ***
## shrub_density:aridity 1 4465 20 376746 0.6263640
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Linear Model AIC:", AIC(n1), "\n")## Linear Model AIC: 309.9796quadratic_model_1 <- glm(animals ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", data = final_data_2022)
quadratic_model_1##
## Call: glm(formula = animals ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 139.405 100.932 -9.768
## aridity I(aridity^2) shrub_density:aridity
## -412.549 117.917 6.412
##
## Degrees of Freedom: 23 Total (i.e. Null); 18 Residual
## Null Deviance: 619900
## Residual Deviance: 186500 AIC: 297.1anova(quadratic_model_1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 619927
## shrub_density 1 26978 22 592949 0.106605
## I(shrub_density^2) 1 2942 21 590007 0.594141
## aridity 1 261283 20 328724 5.12e-07 ***
## I(aridity^2) 1 89956 19 238768 0.003213 **
## shrub_density:aridity 1 52272 18 186496 0.024696 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Quadratic Model AIC:", AIC(quadratic_model_1), "\n")## Quadratic Model AIC: 297.1038if (AIC(quadratic_model_1) < AIC(n1)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}## Quadratic model is better.abund_dens_2022 <- ggplot(final_data_2022, aes(shrub_density, animals)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "A") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
### Richness
n2 <- glm(richness ~ shrub_density * aridity, family = "gaussian", data = final_data_2022)
n2##
## Call: glm(formula = richness ~ shrub_density * aridity, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density aridity
## -0.34946 0.08662 2.58054
## shrub_density:aridity
## -0.01174
##
## Degrees of Freedom: 23 Total (i.e. Null); 20 Residual
## Null Deviance: 475
## Residual Deviance: 132.4 AIC: 119.1anova(n2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 474.96
## shrub_density 1 6.43 22 468.53 0.3243
## aridity 1 335.94 21 132.58 1.043e-12 ***
## shrub_density:aridity 1 0.22 20 132.36 0.8546
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Linear Model AIC:", AIC(n2), "\n")## Linear Model AIC: 119.0888quadratic_model_2 <- glm(richness ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", data = final_data_2022)
quadratic_model_2##
## Call: glm(formula = richness ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 1.9865 -0.7054 0.0776
## aridity I(aridity^2) shrub_density:aridity
## -3.8810 1.6566 -0.0559
##
## Degrees of Freedom: 23 Total (i.e. Null); 18 Residual
## Null Deviance: 475
## Residual Deviance: 87.2 AIC: 113.1anova(quadratic_model_2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 474.96
## shrub_density 1 6.431 22 468.53 0.24925
## I(shrub_density^2) 1 106.430 21 362.10 2.769e-06 ***
## aridity 1 247.045 20 115.05 9.248e-13 ***
## I(aridity^2) 1 23.883 19 91.17 0.02639 *
## shrub_density:aridity 1 3.972 18 87.20 0.36517
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Quadratic Model AIC:", AIC(quadratic_model_2), "\n")## Quadratic Model AIC: 113.0717if (AIC(quadratic_model_2) < AIC(n2)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}## Quadratic model is better.rich_dens_2022 <- ggplot(final_data_2022, aes(shrub_density, richness)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Richness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "B") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
### Evenness
n3 <- glm(Average_Evenness ~ shrub_density * aridity, data = final_data_2022)
n3##
## Call: glm(formula = Average_Evenness ~ shrub_density * aridity, data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density aridity
## 0.019618 -0.001800 0.030276
## shrub_density:aridity
## 0.001651
##
## Degrees of Freedom: 23 Total (i.e. Null); 20 Residual
## Null Deviance: 0.1455
## Residual Deviance: 0.05188 AIC: -69.18anova(n3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 0.145519
## shrub_density 1 0.007333 22 0.138186 0.0927 .
## aridity 1 0.081917 21 0.056269 1.913e-08 ***
## shrub_density:aridity 1 0.004391 20 0.051878 0.1932
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Linear Model AIC:", AIC(n3), "\n")## Linear Model AIC: -69.17676quadratic_model_3 <- glm(Average_Evenness ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", data = final_data_2022)
quadratic_model_3##
## Call: glm(formula = Average_Evenness ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 0.0446964 -0.0295177 0.0027085
## aridity I(aridity^2) shrub_density:aridity
## -0.0385879 0.0178654 0.0002091
##
## Degrees of Freedom: 23 Total (i.e. Null); 18 Residual
## Null Deviance: 0.1455
## Residual Deviance: 0.03225 AIC: -76.59anova(quadratic_model_3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 0.145519
## shrub_density 1 0.007333 22 0.138186 0.04307 *
## I(shrub_density^2) 1 0.056383 21 0.081804 2.027e-08 ***
## aridity 1 0.047100 20 0.034703 2.941e-07 ***
## I(aridity^2) 1 0.002397 19 0.032306 0.24743
## shrub_density:aridity 1 0.000056 18 0.032251 0.86020
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Quadratic Model AIC:", AIC(quadratic_model_2), "\n")## Quadratic Model AIC: 113.0717if (AIC(quadratic_model_2) < AIC(n2)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}## Quadratic model is better.even_dens_2022 <- ggplot(final_data_2022, aes(shrub_density, Average_Evenness)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density per 20m Radius", y = "Evenness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10))+
labs(tag = "C") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
plot <- abund_dens_2022/rich_dens_2022/even_dens_2022
plot
### Temperature (Hold off on this and ask chris)
t1 <- glm(animals ~ shrub_density * mean_temp, family = "gaussian", data = final_data_2022)
t1##
## Call: glm(formula = animals ~ shrub_density * mean_temp, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density mean_temp
## 641.3669 22.3664 -20.8447
## shrub_density:mean_temp
## -0.6221
##
## Degrees of Freedom: 23 Total (i.e. Null); 20 Residual
## Null Deviance: 619900
## Residual Deviance: 389100 AIC: 310.8anova(t1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 619927
## shrub_density 1 26978 22 592949 0.238985
## mean_temp 1 199846 21 393103 0.001351 **
## shrub_density:mean_temp 1 3970 20 389134 0.651496
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Linear Model AIC:", AIC(t1), "\n")## Linear Model AIC: 310.756quadratic_temp_1 <- glm(animals ~ shrub_density + I(shrub_density^2) + mean_temp + I(mean_temp^2) + shrub_density:mean_temp, family = "gaussian", data = final_data_2022)
quadratic_temp_1##
## Call: glm(formula = animals ~ shrub_density + I(shrub_density^2) +
## mean_temp + I(mean_temp^2) + shrub_density:mean_temp, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 4363.574 155.190 -6.801
## mean_temp I(mean_temp^2) shrub_density:mean_temp
## -305.916 5.317 -2.618
##
## Degrees of Freedom: 23 Total (i.e. Null); 18 Residual
## Null Deviance: 619900
## Residual Deviance: 301200 AIC: 308.6anova(quadratic_temp_1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 619927
## shrub_density 1 26978 22 592949 0.2042037
## I(shrub_density^2) 1 2942 21 590007 0.6750263
## mean_temp 1 240180 20 349827 0.0001516 ***
## I(mean_temp^2) 1 2772 19 347055 0.6840117
## shrub_density:mean_temp 1 45820 18 301235 0.0979909 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Quadratic Model AIC:", AIC(quadratic_temp_1), "\n")## Quadratic Model AIC: 308.6113Shrub Density 2023 (Linear better for 2023)
final_data_2023 <- final_data %>%
filter(year == "2023")
x1 <- glm(animals ~ shrub_density * aridity, family = "gaussian", data = final_data_2023)
x1##
## Call: glm(formula = animals ~ shrub_density * aridity, family = "gaussian",
## data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density aridity
## 8.32431 1.85904 2.30955
## shrub_density:aridity
## -0.05488
##
## Degrees of Freedom: 21 Total (i.e. Null); 18 Residual
## Null Deviance: 12650
## Residual Deviance: 10190 AIC: 207.5anova(x1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 12649
## shrub_density 1 2257.72 20 10391 0.04579 *
## aridity 1 199.04 19 10192 0.55317
## shrub_density:aridity 1 4.64 18 10187 0.92782
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Linear Model AIC:", AIC(x1), "\n")## Linear Model AIC: 207.4663quadratic_model_x1 <- glm(animals ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", data = final_data_2023)
quadratic_model_x1##
## Call: glm(formula = animals ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian",
## data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## -1.65760 3.43942 -0.15541
## aridity I(aridity^2) shrub_density:aridity
## 29.97243 -7.06338 0.03991
##
## Degrees of Freedom: 21 Total (i.e. Null); 16 Residual
## Null Deviance: 12650
## Residual Deviance: 9619 AIC: 210.2anova(quadratic_model_x1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 12648.8
## shrub_density 1 2257.72 20 10391.1 0.05264 .
## I(shrub_density^2) 1 55.46 19 10335.6 0.76135
## aridity 1 317.46 18 10018.1 0.46743
## I(aridity^2) 1 396.75 17 9621.4 0.41659
## shrub_density:aridity 1 1.93 16 9619.5 0.95482
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Quadratic Model AIC:", AIC(quadratic_model_x1), "\n")## Quadratic Model AIC: 210.2043if (AIC(quadratic_model_x1) < AIC(x1)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}## Linear model is better or equally good.abund_dens_2023 <- ggplot(final_data_2023, aes(shrub_density, animals)) +
geom_smooth(method = "lm", se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "A") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
abund_dens_2023## `geom_smooth()` using formula = 'y ~ x'
x2 <- glm(richness ~ shrub_density * aridity, data = final_data_2023)
x2##
## Call: glm(formula = richness ~ shrub_density * aridity, data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density aridity
## 4.775057 0.220144 -0.167510
## shrub_density:aridity
## -0.009058
##
## Degrees of Freedom: 21 Total (i.e. Null); 18 Residual
## Null Deviance: 95.32
## Residual Deviance: 67.28 AIC: 97.03anova(x2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 95.318
## shrub_density 1 25.4984 20 69.820 0.009006 **
## aridity 1 2.4110 19 67.409 0.421898
## shrub_density:aridity 1 0.1265 18 67.282 0.854019
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Linear Model AIC:", AIC(x2), "\n")## Linear Model AIC: 97.02606quadratic_model_x2 <- glm(richness ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, data = final_data_2023)
quadratic_model_x2##
## Call: glm(formula = richness ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 3.32139 -0.05358 0.02657
## aridity I(aridity^2) shrub_density:aridity
## 3.87515 -1.02486 -0.02127
##
## Degrees of Freedom: 21 Total (i.e. Null); 16 Residual
## Null Deviance: 95.32
## Residual Deviance: 59.25 AIC: 98.23anova(quadratic_model_x2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 95.318
## shrub_density 1 25.4984 20 69.820 0.008689 **
## I(shrub_density^2) 1 0.4031 19 69.417 0.741448
## aridity 1 2.0180 18 67.399 0.460389
## I(aridity^2) 1 7.6014 17 59.797 0.151936
## shrub_density:aridity 1 0.5479 16 59.249 0.700486
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Quadratic Model AIC:", AIC(quadratic_model_x2), "\n")## Quadratic Model AIC: 98.22897if (AIC(quadratic_model_x2) < AIC(x2)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}## Linear model is better or equally good.rich_dens_2023 <- ggplot(final_data_2023, aes(shrub_density, richness)) +
geom_smooth(method = "lm", se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Richness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "B") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
rich_dens_2023## `geom_smooth()` using formula = 'y ~ x'
x3 <- glm(Average_Evenness ~ shrub_density * aridity, data = final_data_2023)
x3##
## Call: glm(formula = Average_Evenness ~ shrub_density * aridity, data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density aridity
## 0.1816823 0.0084948 -0.0469045
## shrub_density:aridity
## -0.0009284
##
## Degrees of Freedom: 21 Total (i.e. Null); 18 Residual
## Null Deviance: 0.1608
## Residual Deviance: 0.009233 AIC: -98.64anova(x3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 0.160797
## shrub_density 1 0.013715 20 0.147082 2.33e-07 ***
## aridity 1 0.136519 19 0.010562 < 2.2e-16 ***
## shrub_density:aridity 1 0.001329 18 0.009233 0.1074
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Linear Model AIC:", AIC(x3), "\n")## Linear Model AIC: -98.63904quadratic_model_x3 <- glm(Average_Evenness ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, data = final_data_2023)
quadratic_model_x3##
## Call: glm(formula = Average_Evenness ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 0.1963905 0.0105327 -0.0001974
## aridity I(aridity^2) shrub_density:aridity
## -0.0877876 0.0103750 -0.0008426
##
## Degrees of Freedom: 21 Total (i.e. Null); 16 Residual
## Null Deviance: 0.1608
## Residual Deviance: 0.008405 AIC: -96.71anova(quadratic_model_x3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 0.160797
## shrub_density 1 0.013715 20 0.147082 3.226e-07 ***
## I(shrub_density^2) 1 0.020159 19 0.126922 5.833e-10 ***
## aridity 1 0.116561 18 0.010361 < 2.2e-16 ***
## I(aridity^2) 1 0.001096 17 0.009265 0.1485
## shrub_density:aridity 1 0.000860 16 0.008405 0.2006
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Quadratic Model AIC:", AIC(quadratic_model_x3), "\n")## Quadratic Model AIC: -96.7066if (AIC(quadratic_model_x1) < AIC(x1)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}## Linear model is better or equally good.even_dens_2023 <- ggplot(final_data_2023, aes(shrub_density, Average_Evenness)) +
geom_smooth(method = "lm", se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density per 20m Radius", y = "Evenness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10))+
labs(tag = "C") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
even_dens_2023## `geom_smooth()` using formula = 'y ~ x'
plot2 <- abund_dens_2023/rich_dens_2023/even_dens_2023
plot2## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'